Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification.

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski's homepage, and the results demonstrate the improved performance of the proposed approach.

How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn, store, and evaluate Mahalanobis matrix efficiently. Besides, a dynamic triplets building strategy is proposed to build a feedback from the obtained Mahalanobis matrix to the triplet constraints, which can further improve the LDMLT algorithm. Furthermore, the proposed method is applied to various applications, including pattern recognition, facial expression recognition, and image retrieval. The results demonstrate the improved performance of the proposed approach.

In this paper, we consider the family of total Bregman divergences (tBDs) as an efficient and robust "distance" measure to quantify the dissimilarity between shapes. We use the tBD-based ℓ₁-norm center as the representative of a set of shapes, and call it the t-center. First, we briefly present and analyze the properties of the tBDs and t-centers following our previous work in. Then, we prove that for any tBD, there exists a distribution which belongs to the lifted exponential family (lEF) of statistical distributions. Further, we show that finding the maximum a posteriori (MAP) estimate of the parameters of the lifted exponential family distribution is equivalent to minimizing the tBD to find the t-centers. This leads to a new clustering technique, namely, the total Bregman soft clustering algorithm. We evaluate the tBD, t-center, and the soft clustering algorithm on shape retrieval applications. Our shape retrieval framework is composed of three steps: 1) extraction of the shape boundary points, 2) affine alignment of the shapes and use of a Gaussian mixture model (GMM) to represent the aligned boundaries, and 3) comparison of the GMMs using tBD to find the best matches given a query shape. To further speed up the shape retrieval algorithm, we perform hierarchical clustering of the shapes using our total Bregman soft clustering algorithm. This enables us to compare the query with a small subset of shapes which are chosen to be the cluster t-centers. We evaluate our method on various public domain 2D and 3D databases, and demonstrate comparable or better results than state-of-the-art retrieval techniques.

Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivity with desirably low false positive (FP) rate is critical for a CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. Given various spurious imagery noises which cause observation uncertainties, this remains a very challenging task. In this paper, we propose a novel, two-tiered coarse-to-fine (CTF) classification cascade framework to tackle this problem. We first obtain classification-critical data samples (e.g., implicit samples on the decision boundary) extracted from the holistic data distributions using a robust parametric model (e.g., [13]); then we build a graph-embedding based nonparametric classifier on sampled data, which can more accurately preserve or formulate the complex classification boundary. These two steps can also be considered as effective "sample pruning" and "feature pursuing + kNN/template matching", respectively. Our approach is validated comprehensively in colorectal polyp detection and lung nodule detection CAD systems, as the top two deadly cancers, using hospital scale, multi-site clinical datasets. The results show that our method achieves overall better classification/detection performance than existing state-of-the-art algorithms using single-layer classifiers, such as the support vector machine variants [17], boosting [15], logistic regression [11], relevance vector machine [13], k-nearest neighbor [9] or spectral projections on graph [2].

The problem of learning a proper distance or similarity metric arises in many applications such as content-based image retrieval. In this work, we propose a boosting algorithm, MetricBoost, to learn the distance metric that preserves the proximity relationships among object triplets: object i is more similar to object j than to object k. Metric-Boost constructs a positive semi-definite (PSD) matrix that parameterizes the distance metric by combining rank-one PSD matrices. Different options of weak models and combination coefficients are derived. Unlike existing proximity preserving metric learning which is generally not scalable, MetricBoost employs a bipartite strategy to dramatically reduce computation cost by decomposing proximity relationships over triplets into pair-wise constraints. Met-ricBoost outperforms the state-of-the-art on two real-world medical problems: 1. identifying and quantifying diffuse lung diseases; 2. colorectal polyp matching between different views, as well as on other benchmark datasets.

Boosting is a well known machine learning technique used to improve the performance of weak learners and has been successfully applied to computer vision, medical image analysis, computational biology and other fields. A critical step in boosting algorithms involves update of the data sample distribution, however, most existing boosting algorithms use updating mechanisms that lead to overfitting and instabilities during evolution of the distribution which in turn results in classification inaccuracies. Regularized boosting has been proposed in literature as a means to overcome these difficulties. In this paper, we propose a novel total Bregman divergence (tBD) regularized LPBoost, termed tBRLPBoost. tBD is a recently proposed divergence in literature, which is statistically robust and we prove that tBRLPBoost requires a constant number of iterations to learn a strong classifier and hence is computationally more efficient compared to other regularized Boosting algorithms. Also, unlike other boosting methods that are only effective on a handful of datasets, tBRLPBoost works well on a variety of datasets. We present results of testing our algorithm on many public domain databases and comparisons to several other state-of-the-art methods. Numerical results show that the proposed algorithm has much improved performance in efficiency and accuracy over other methods.

Divergence measures provide a means to measure the pairwise dissimilarity between "objects," e.g., vectors and probability density functions (pdfs). Kullback-Leibler (KL) divergence and the square loss (SL) function are two examples of commonly used dissimilarity measures which along with others belong to the family of Bregman divergences (BD). In this paper, we present a novel divergence dubbed the Total Bregman divergence (TBD), which is intrinsically robust to outliers, a very desirable property in many applications. Further, we derive the TBD center, called the t-center (using the l(1)-norm), for a population of positive definite matrices in closed form and show that it is invariant to transformation from the special linear group. This t-center, which is also robust to outliers, is then used in tensor interpolation as well as in an active contour based piecewise constant segmentation of a diffusion tensor magnetic resonance image (DT-MRI). Additionally, we derive the piecewise smooth active contour model for segmentation of DT-MRI using the TBD and present several comparative results on real data.

Shape database search is ubiquitous in the world of bio-metric systems, CAD systems etc. Shape data in these domains is experiencing an explosive growth and usually requires search of whole shape databases to retrieve the best matches with accuracy and efficiency for a variety of tasks. In this paper, we present a novel divergence measure between any two given points in Rnor two distribution functions. This divergence measures the orthogonal distance between the tangent to the convex function (used in the definition of the divergence) at one of its input arguments and its second argument. This is in contrast to the ordinate distance taken in the usual definition of the Bregman class of divergences. We use this orthogonal distance to redefine the Bregman class of divergences and develop a new theory for estimating the center of a set of vectors as well as probability distribution functions. The new class of divergences are dubbed the total Bregman divergence (TBD). We present the l\-norm based TBD center that is dubbed the t-center which is then used as a cluster center of a class of shapes The t-center is weighted mean and this weight is small for noise and outliers. We present a shape retrieval scheme using TBD and the t-center for representing the classes of shapes from the MPEG-7 database and compare the results with other state-of-the-art methods in literature.