Adversarial pruning compresses models while preserving robustness. Current methods require access to adversarial examples during pruning. This significantly hampers training efficiency. Moreover, as new adversarial attacks and training methods develop at a rapid rate, adversarial pruning methods need to be modified accordingly to keep up. In this work, we propose a novel framework to prune a previously trained robust neural network while maintaining adversarial robustness, without further generating adversarial examples. We leverage concurrent self-distillation and pruning to preserve knowledge in the original model as well as regularizing the pruned model via the Hilbert-Schmidt Information Bottleneck. We comprehensively evaluate our proposed framework and show its superior performance in terms of both adversarial robustness and efficiency when pruning architectures trained on the MNIST, CIFAR-10, and CIFAR-100 datasets against five state-of-the-art attacks..

We study the problem of ranking regression, in which a dataset of rankings is used to learn Plackett–Luce scores as functions of sample features. We propose a novel spectral algorithm to accelerate learning in ranking regression. Our main technical contribution is to show that the Plackett–Luce negative log-likelihood augmented with a proximal penalty has stationary points that satisfy the balance equations of a Markov Chain. This allows us to tackle the ranking regression problem via an efficient spectral algorithm by using the Alternating Directions Method of Multipliers (ADMM). ADMM separates the learning of scores and model parameters, and in turn, enables us to devise fast spectral algorithms for ranking regression via both shallow and deep neural network (DNN) models. For shallow models, our algorithms are up to 579 times faster than the Newton’s method. For DNN models, we extend the standard ADMM via a Kullback–Leibler proximal penalty and show that this is still amenable to fast inference via a spectral approach. Compared to a state-of-the-art siamese network, our resulting algorithms are up to 175 times faster and attain better predictions by up to 26% Top-1 Accuracy and 6% Kendall-Tau correlation over five real-life ranking datasets.

Graph embeddings have been tremendously successful at producing node representations that are discriminative for downstream tasks. In this paper, we study the problem of graph transfer learning: given two graphs and labels in the nodes of the first graph, we wish to predict the labels on the second graph. We propose a tractable, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random.

In lifelong learning, we wish to maintain and update a model (e.g., a neural network classifier) in the presence of new classification tasks that arrive sequentially. In this paper, we propose a learn-prune-share (LPS) algorithm which addresses the challenges of catastrophic forgetting, parsimony, and knowledge reuse simultaneously. LPS splits the network into task-specific partitions via an ADMM-based pruning strategy. This leads to no forgetting, while maintaining parsimony. Moreover, LPS integrates a novel selective knowledge sharing scheme into this ADMM optimization framework. This enables adaptive knowledge sharing in an end-to-end fashion. Comprehensive experimental results on two lifelong learning benchmark datasets and a challenging real world radio frequency fingerprinting dataset are provided to demonstrate the effectiveness of our approach. Our experiments show that LPS consistently outperforms multiple state-of-the-art competitors.

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two critical challenges to addressing this paradigm: (a) transferring knowledge from old to new classes, and (b) incorporating knowledge learned from new classes back to the original model. We propose Class Discovery Kernel Network with Expansion (CD-KNet-Exp), a deep learning framework, which utilizes the Hilbert Schmidt Independence Criterion to bridge supervised and unsupervised information together in a systematic way, such that the learned knowledge from old classes is distilled appropriately for discovering new classes. Compared to competing methods, CD-KNet-Exp shows superior performance on three publicly available benchmark datasets and a challenging real-world radio frequency fingerprinting dataset.

Climate change is one of the major challenges to human beings in our time. It brings many unexpected disasters which cause drastic losses including lives and properties. To better understand climate change, scientists developed various Global Climate Models (GCMs) to simulate the global climate and make projections for future climate values. These global climate models have coarse grids (i.e., low resolutions both in space and time) due to limitations of computing power and simulation time. Although they are helpful in predicting large scale long term trend in climate, they are too coarse for impact analysis in smaller scales such as in regional or local scale. However, climate conditions in regional or local scale are very important in making decisions related to climate conditions such as infrastructure, transportation and evacuation, as they highly depend on small scale climate conditions. In this paper, we proposed YNet, a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables, on multiple GCMs directly rather than on reanalysis data. We analyzed and compared our proposed method with four other methods on datasets of three climate variables: mean precipitation, and extreme values (maximum temperature and minimum temperature). The results show the effectiveness of the proposed method.

We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by and are at least as expressive as spectral clustering. Our training objective, based on the Hilbert Schmidt Independence Criterion, can be optimized via gradient adaptations on the Stiefel manifold, leading to significant acceleration over spectral methods relying on eigen-decompositions. Finally, our trained embedding can be directly applied to out-of-sample data. We show experimentally that our approach outperforms several state-of-the-art deep clustering methods, as well as traditional approaches such as k-means and spectral clustering over a broad array of real and synthetic datasets.

Climate prediction is a very challenging problem. Many institutes around the world try to predict climate variables by building climate models called General Circulation Models (GCMs), which are based on mathematical equations that describe the physical processes. The prediction abilities of different GCMs may vary dramatically across different regions and time. Motivated by the need of identifying which GCMs are more useful for a particular region and time, we introduce a clustering model combining Dirichlet Process (DP) mixture of sparse linear regression with Markov Random Fields (MRFs). This model incorporates DP to automatically determine the number of clusters, imposes MRF constraints to guarantee spatio-temporal smoothness, and selects a subset of GCMs that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. Experimental results are provided for both synthetic and real-world climate data.

Retinopathy of Prematurity (ROP) is a leading cause for childhood blindness worldwide. An automated ROP detection system could significantly improve the chance of a child receiving proper diagnosis and treatment. We propose a means of producing a continuous severity score in an automated fashion, regressed from both (a) diagnostic class labels as well as (b) comparison outcomes. Our generative model combines the two sources, and successfully addresses inherent variability in diagnostic outcomes. In particular, our method exhibits an excellent predictive performance of both diagnostic and comparison outcomes over a broad array of metrics, including AUC, precision, and recall.

Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size. We study a natural experimental design objective, namely, D-optimality, that can be used to identify which K pairwise comparisons to generate. This objective is known to perform well in practice, and is submodular, making the selection approximable via the greedy algorithm. A naïve greedy implementation has O(N2 d2 K) complexity, where N is the dataset size, d is the feature space dimension, and K is the number of generated comparisons. We show that, by exploiting the inherent geometry of the dataset–namely, that it consists of pairwise comparisons–the greedy algorithm's complexity can be reduced to O(N2 (K + d) + N(dK + d2) + d2 K). We apply the same acceleration also to the so-called lazy greedy algorithm. When combined, the above improvements lead to an execution time of less than 1 hour for a dataset with 108 comparisons; the naïve greedy algorithm on the same dataset would require more than 10 days to terminate.

Machine learning has seen tremendous growth in recent years thanks to two key advances in technology: massive data generation and highly-parallel accelerator architectures. The rate that data is being generated is exploding across multiple domains, including medical research, environmental science, web-search, and e-commerce. Many of these advances have benefited from emergent web-based applications, and improvements in data storage and sensing technologies. Innovations in parallel accelerator hardware, such as GPUs, has made it possible to process massive amounts of data in a timely fashion. Given these advanced data acquisition technology and hardware, machine learning researchers are equipped to generate and sift through much larger and complex datasets quickly. In this work, we focus on accelerating Kernel Dimension Alternative Clustering algorithms using GPUs. We conduct a thorough performance analysis by using both synthetic and real-world datasets, while also modifying both the structure of the data, and the size of the datasets. Our GPU implementation reduces execution time from minutes to seconds, which enables us to develop a web-based application for users to, interactively, view alternative clustering solutions.

In recent years, the availability of data-driven analytics has become a key tool in discovery in public health and environmental science research. As a result, these communities have looked to leverage recent advances in machine learning algorithms. This class of algorithms are able to find hidden patterns and develop new knowledge in complex data, accelerating the rate of discovery in multiple research domains. In this paper, we present our methodology of applying machine learning algorithms to health outcomes, chemical exposures, and social behavior data from expectant mothers, as part of the NIEHS-supported PROTECT Center. The ultimate goal is to determine the dominant factors/features potentially responsible for the high rate of premature births in Puerto Rico.Many commonly-used machine learning algorithms can be used for feature selection. However, given the imbalance in our birth outcome data, with many more term (i.e., 37 weeks or longer) versus preterm pregnancies (i.e., less than 37 weeks), analysis of the PROTECT dataset presents many unique challenges. In addition to outcome imbalance, our database contains both quantitative and categorical data variables, adding some complexity to the analytical methods used. Applying straightforward correlation or regression analysis would be insufficient. Our datasets also contain a significant amount of missing data (incomplete records), providing noisy input to our algorithms. A further challenge is that we are working with a relatively limited set of complex data (only 2000 participants to date), so our models must be able to be built with a relatively small number of data samples.To overcome these challenges, we have implemented a cus-tomized end-to-end analytical toolchain which forms a pre-processing pipeline. Our framework performs general data filtering and handles missing data fields using a similarity-based approach. Next, we apply one of a number of different machine learning algorithms, including Linear Correlation, Normalized Mutual Information, Logistic Regression, and Decision Trees. We use these during both feature selection and model performance evaluation. Finally, we present top-ranked features produced by our model as potential key contributors of high preterm birth rates in Puerto Rico, and discuss results across these algorithms.

Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S&EE) applications where risk management is a core component, well-characterized uncertainty estimates must accompany predictions. However, S&E observations and model-simulations often follow heavily skewed distributions and are not well modeled with DL approaches, since they usually optimize a Gaussian, or Euclidean, likelihood loss. Recent developments in Bayesian Deep Learning (BDL), which attempts to capture uncertainties from noisy observations, aleatoric, and from unknown model parameters, epistemic, provide us a foundation. Here we present a discrete-continuous BDL model with Gaussian and lognormal likelihoods for uncertainty quantification (UQ). We demonstrate the approach by developing UQ estimates on "DeepSD'', a super-resolution based DL model for Statistical Downscaling (SD) in climate applied to precipitation, which follows an extremely skewed distribution. We find that the discrete-continuous models outperform a basic Gaussian distribution in terms of predictive accuracy and uncertainty calibration. Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes. Such results may be important across S&E, as well as other domains such as finance and economics, where extremes are often of significant interest. Furthermore, to our knowledge, this is the first UQ model in SD where both aleatoric and epistemic uncertainties are characterized.

Clustering is a challenging problem because given the same data set, it can be grouped in multiple different ways. Which of these clustering solutions is interesting depends on its domain application. Thus, incorporating domain expert input often improves clustering performance. However, most existing semi-supervised clustering techniques can only incorporate instance-level constraints (a few labels or must-link/cannot-link constraints), which domain experts may not be comfortable providing in knowledge discovery problems because categories are not known. Fortunately, domain experts often have an idea regarding properties that clustering solutions should have in order to be useful in domain application based on domain relevant scores. In this paper, we provide a framework for jointly optimizing the usefulness and quality of a clustering solution. Experiments on a synthetic data, a benchmark data, and a real-world disease subtyping problem demonstrate the usefulness of our proposed approach.

Clustering is a technique that is usually applied as a tool for exploratory data analysis. Because of the exploratory nature of this task, it would be beneficial if a clustering method generates interpretable results, and allows incorporating domain knowledge. This motivates us to develop a probabilistic discriminative model that learns a rectangular decision rule for each cluster, we call Discriminative Rectangle Mixture (DReaM) model. DReaM gives interpretable clustering results, because the rectangular decision rules discovered explicitly illustrate how one cluster is defined and differs from other clusters. It also facilitates us to take advantage of existing rules because we can choose informative prior distributions for the rectangular rules. Moreover, DReaM allows that the features for generating rules do not have to be the same as the features for discovering cluster structure. We approximate the distribution for the rules discovered via variational inference. Experimental results demonstrate that DReaM gives more interpretable clustering results, and yet its performance is comparable to existing clustering methods when solving traditional clustering. Furthermore, in real applications, DReaM is able to effectively take advantage of domain knowledge, and to generate reasonable clustering results.

Visual detection of lesional areas on a cortical surface is critical in rendering a successful surgical operation for Treatment Resistant Epilepsy (TRE) patients. Unfortunately, 45% of Focal Cortical Dysplasia (FCD, the most common kind of TRE) patients have no visual abnormalities in their brains' 3D-MRI images. We collaborate with doctors from NYU Langone's Comprehensive Epilepsy Center and apply machine learning methodologies to identify the resective zones for these {MRI-negative} FCD patients. Our task is particularly challenging because MRI images can only provide a limited number of features. Furthermore, data from different patients often exhibit inter-patient variabilities due to age, gender, left/right handedness, etc. In this paper, we introduce a new approach which combines the restricted Boltzmann machines and a Bayesian non-parametric mixture model to address these issues. We demonstrate the efficacy of our model by applying it to a retrospective dataset of MRI-negative FCD patients who are seizure free after surgery.

Predicting disease course is critical in chronic progressive diseases such as multiple sclerosis (MS) for determining treatment. Forming an accurate predictive model based on clinical data is particularly challenging when data is gathered from multiple clinics/physicians as the labels vary with physicians' subjective judgment about clinical tests and further we have no a priori knowledge of the various types of physician subjectivity. At the same time, we often have some (limited) domain knowledge on how to group patients into disease progression subgroups. In this paper, we first present our rationale for choosing a Dirichlet mixture of Gaussian processes (DPMGP) model to address the subjectivity in our data. We then introduce a new approach to incorporating domain knowledge into the non-parametric mixture model. We demonstrate the efficacy of our model by applying it to two medical datasets to predict disease progression in MS patients and disability levels in early Parkinson's patients.

Clustering and ranking have been successfully applied independently to homogeneous information networks, containing only one type of objects. However, real-world information networks are oftentimes heterogeneous, containing multiple types of objects and links. Recent research has shown that clustering and ranking can actually mutually enhance each other, and several techniques have been developed to integrate clustering and ranking together on a heterogeneous information network. To the best our knowledge, however, all of such techniques assume the network follows a certain schema. In this paper, we propose a probabilistic generative model that simultaneously achieves clustering and ranking on a heterogeneous network that can follow arbitrary schema, where the edges from different types are sampled from a Poisson distribution with the parameters determined by the ranking scores of the nodes in each cluster. A variational Bayesian inference method is proposed to learn these parameters, which can be used to output ranking and clusters simultaneously. Our method is evaluated on both synthetic and real-world networks extracted from the DBLP and YELP data. Experimental results show that our method outperforms the state-of-the-art baselines.

Chronic obstructive pulmonary disease (COPD) is a lung disease characterized by airflow limitation usually associated with an inflammatory response to noxious particles, such as cigarette smoke. COPD is currently the third leading cause of death in the United States and is the only leading cause of death that is increasing in prevalence. It also represents an enormous financial burden to society, costing tens of billions of dollars annually in the U.S. It is widely accepted by the medical community that COPD is a heterogeneous disease, with substantial evidence indicating that genetic variation contributes to varying levels of disease susceptibility. This heterogeneity makes it difficult to predict health decline and develop targeted treatments for better patient care. Although researchers have made several attempts to discover disease subtypes, results have been inconclusive, in part because standard clustering methods have not properly dealt with disease manifestations that may worsen with increased exposure. In this paper we introduce a transformative way of looking at the COPD subtyping task. Specifically, we model the relationship between risk factors (such as age and smoke exposure) and manifestations of disease severity using Gaussian Processes, which allow us to represent so-called "disease trajectories". We also posit that individuals can be associated with multiple disease types (latent clusters), which we assume are influenced by genetics. Furthermore, we predict that only subsets of the numerous disease-related quantitative features are useful for describing each latent subtype. We model these associations using two separate beta process priors, and we describe a variational inference approach to discover the most probable latent cluster assignments. Results are validated with associations to genetic markers.

Utilizing the concept of hypothesis margins to measure the quality of a set of features has been a growing line of research in the last decade. However, most previous algorithms have been developed under the large hypothesis margin principles of the 1-NN algorithm, such as Simba. Little attention has been paid so far to exploiting the hypothesis margins of boosting to evaluate features. Boosting is well known to maximize the training examples' hypothesis margins, in particular, the average margins which are known to be the first statistics that considers the whole margin distribution. In this paper, we describe how to utilize the training examples' mean margins of boosting to select features. A weight criterion, termed Margin Fraction (MF), is assigned to each feature that contributes to the average margin distribution combined in the final output produced by boosting. Applying the idea of MF to a sequential backward selection method, a new embedded selection algorithm is proposed, called SBS-MF. Experimentation is carried out using different data sets, which compares the proposed SBS-MF with two boosting based feature selection approaches, as well as to Simba. The results show that SBS-MF is effective in most of the cases.

Effective outlier detection requires the data to be described by a set of features that captures the behavior of normal data while emphasizing those characteristics of outliers which make them different than normal data. In this work, we present a novel non-parametric evaluation criterion for filter-based feature selection which caters to outlier detection problems. The proposed method seeks the subset of features that represents the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared to popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit (GPU) to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points.

Medical coding or classification is the process of transforming information contained in patient medical records into standard predefined medical codes. There are several worldwide accepted medical coding conventions associated with diagnoses and medical procedures; however, in the United States the Ninth Revision of ICD(ICD-9) provides the standard for coding clinical records. Accurate medical coding is important since it is used by hospitals for insurance billing purposes. Since after discharge a patient can be assigned or classified to several ICD-9 codes, the coding problem can be seen as a multi-label classification problem. In this paper, we introduce a multi-label large-margin classifier that automatically learns the underlying inter-code structure and allows the controlled incorporation of prior knowledge about medical code relationships. In addition to refining and learning the code relationships, our classifier can also utilize this shared information to improve its performance. Experiments on a publicly available dataset containing clinical free text and their associated medical codes showed that our proposed multi-label classifier outperforms related multi-label models in this problem.

A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the original features are important. There is a recent interest in sparse PCA (SPCA). By applying an L1 regularizer to PCA, a sparse transformation is achieved. However, true feature selection may not be achieved as non-sparse coefficients may be distributed over several features. Feature selection is an NP-hard combinatorial optimization problem. This paper relaxes and re-formulates the feature selection problem as a convex continuous optimization problem that minimizes a mean-squared-reconstruction error (a criterion optimized by PCA) and considers feature redundancy into account (an important property in PCA and feature selection). We call this new method Convex Principal Feature Selection (CPFS). Experiments show that CPFS performed better than SPCA in selecting features that maximize variance or minimize the mean-squared-reconstruction error.

Real-world applications often involve complex data that can be interpreted in many different ways. When clustering such data, there may exist multiple groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. However, traditional clustering is restricted to finding only one single clustering of the data. In this article, we propose a new clustering paradigm for exploratory data analysis: find all non-redundant clustering solutions of the data, where data points in the same cluster in one solution can belong to different clusters in other partitioning solutions. We present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. In essence, both approaches find alternative ways to partition the data by projecting it to a space that is orthogonal to the current solution. The first approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. We study the relationship between the two approaches. We also combine our framework with techniques for automatically finding the number of clusters in the different solutions, and study stopping criteria for determining when all meaningful solutions are discovered. We test our framework on both synthetic and high-dimensional benchmark data sets, and the results show that indeed our approaches were able to discover varied clustering solutions that are interesting and meaningful.

In an idealized gated radiotherapy treatment, radiation is delivered only when the tumor is at the right position. For gated lung cancer radiotherapy, it is difficult to generate accurate gating signals due to the large uncertainties when using external surrogates and the risk of pneumothorax when using implanted fiducial markers. In this paper, we investigate machine learning algorithms for markerless gated radiotherapy with fluoroscopic images. Previous approach utilizes template matching to localize the tumor position. Here, we investigate two ways to improve the precision of tumor target localization by applying: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. Template matching only considers images inside the gating window, but images outside the gating window might provide additional information. We take advantage of both states and re-cast the gating problem into a classification problem. Thus, we are able to use the SVM classifier for gated radiotherapy. To verify the effectiveness of the two proposed techniques, we apply them on five sequences of fluoroscopic images from five lung cancer patients against the gating signal of manually contoured tumors as ground truth. Our five-patient case study shows that both ensemble template matching and SVM are reasonable tools for image-guided markerless gated radiotherapy with an average of approximately 95% precision in terms of delivered target dose at approximately 35% duty cycle.

Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can often be interpreted in many different ways; data can have different groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. Why commit to one clustering solution while all these alternative clustering views might be interesting to the user. In this paper, we propose a new clustering paradigm for explorative data analysis: find all non-redundant clustering views of the data, where data points of one cluster can belong to different clusters in other views. We present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. In essence, both approaches find alternative ways to partition the data by projecting it to a space that is orthogonal to our current solution. The first approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. We test our framework on both synthetic and high-dimensional benchmark data sets, and the results show that indeed our approaches were able to discover varied solutions that are interesting and meaningful. keywords: multi-view clustering, non-redundant clustering, orthogonalization

Computer architects utilize simulation tools to evaluate the merits of a new design feature. The time needed to adequately evaluate the tradeoffs associated with adding any new feature has become a critical issue. Recent work has found that by identifying execution phases present in common workloads used in simulation studies, we can apply clustering algorithms to significantly reduce the amount of time needed to complete the simulation. Our goal in this paper is to demonstrate the value of this approach when applied to the set of industry-standard benchmarks most commonly used in computer architecture studies. We also look to improve upon prior work by applying more appropriate clustering algorithms to identify phases, and to further reduce simulation time.We find that the phase clustering in computer architecture simulation has many similarities to text clustering. In prior work on clustering techniques to reduce simulation time, K-means clustering was used to identify representative program phases. In this paper we apply a mixture of multinomials to the clustering problem and show its advantages over using K-means on simulation data. We have implemented these two clustering algorithms and evaluate how well they can characterize program behavior. By adopting a mixture of multinomials model, we find that we can maintain simulation result fidelity, while greatly reducing overall simulation time. We report results for a range of applications taken from the SPEC2000 benchmark suite.

For many feature selection problems, a human denes the features that are potentially useful, and then a subset is chosen from the original pool of features using an automated feature selection algorithm. In contrast to supervised learning, class information is not available to guide the feature search for unsupervised learning tasks. In this paper, we introduce Visual-FSSEM (Visual Feature Subset Selection using Expectation-Maximization Clustering), which incorporates visualization techniques, clustering, and user interaction to guide the feature subset search and to enable a deeper understanding of the data. Visual-FSSEM, serves both as an exploratory and multivariate-data visualization tool. We illustrate Visual-FSSEM on a high-resolution computed tomography lung image data set. 1. INTRODUCTION Most research in unsupervised clustering assumes that when creating the target data set, the data analyst in conjunction with the domain expert was able to identify a small relevant set of ...