In realistic acoustic scenes, the detection of particular types of environmental sounds is often impeded by the simultaneous presence of multiple sound sources. In this work, we use simulations to systematically investigate the impact of superimposed distractor sources on sound type classification in a binaural robotic system and suggest techniques for increasing the robustness under such conditions. First, we demonstrate that by superimposing target sounds with strongly varying general environmental sounds during training, sound type classifiers are less affected by the presence of a distractor source. Moreover, we show that generalization performance of such models depends on how similar the angular source configuration and the signal-to-noise ratio are to the conditions under which the models were trained. Based on these results, we demonstrate how robust models can be obtained by including a variety of different conditions in the training data, a procedure called multi-conditional training. We evaluate this technique training both with ambient sources as well as with point sources with varying angular configurations, and show that this is an effective approach to produce models with close-to-optimal performance under a wide range of conditions. Moreover, we investigate the impact of head orientation and find that it has a significant influence on classification performance.

Pairwise clustering methods are able to handle relational data, in which a set of objects is described via a matrix of pairwise (dis)similarities. Using the framework of source coding, it has been shown that pairwise clustering can be considered as entropy maximization problem under the constraint of keeping the distortion at a small value. This can be optimized via deterministic annealing. For the purpose of improving this optimization procedure, we have previously suggested two incremental pairwise clustering methods. However, they either only allow an even number of clusters, or cannot be applied to large proximity matrices. In this paper, we propose an incremental pairwise clustering method that resolves these issues. We compare the computational efficiency of the proposed algorithm to the previous incremental methods using simulations. Moreover, we apply the method to identify functionally connected brain networks by clustering a high-dimensional connectivity matrix obtained from resting state functional magnetic resonance imaging data.

How can we test for group differences in multidimensional input patterns, such as functional magnetic resonance imaging measurements or gene expression values? One solution is to split the available data into training and test set, and to estimate the generalization accuracy of a classifier that predicts the group variable from the input pattern. If this lies significantly above chance level, we can reject the null hypothesis of no association. This test is straightforward for balanced data, where all groups are equally frequent in the data set. However, data sets collected in observational studies are often imbalanced. Then accuracy is no longer a suitable measure of performance, and balanced accuracy should be used instead. In this paper, we give an overview on existing analytical tests and use the framework of prediction theory to derive a new test for the balanced accuracy of a classifier. We then use numerical simulations to evaluate the type I error rate and the power of two tests for imbalanced data.

We suggest a multivariate genotype-phenotype association test for functional magnetic resonance imaging (fMRI) data. The method uses a voxel selection and ranking scheme based on iterative adaptive Lasso for defining a functional region of interest. A classifier-based test is used to assess the significance of potential associations between the differential activity pattern within that region and a set of candidate genetic variants. We applied the method to a small sample dataset from an ongoing imaging genetics study and identified a significant genetic association to a stimulus-locked imaging phenotype.

Pairwise clustering methods are able to handle relational data, in which a set of objects is described via a matrix of pairwise (dis)similarities. Here, we consider a cost function for pairwise clustering which maximizes model entropy under the constraint that the error for reconstructing objects from class information is fixed to a small value. Based on the analysis of structural transitions, we derive a new incremental pairwise clustering method which increases the number of clusters until a certain value of a Lagrange multiplier is reached. In addition, the calculation of phase transitions is used for speed-up. The incremental duplication of clusters helps to avoid local optima, and the stopping criterion automatically determines the number of clusters. The performance of the method is assessed on artificial and real-world data.

The analysis of microarray data is a challenging task for statistical and machine learning methods, since the datasets usually contain a very large number of features (genes) and only a small number of examples (subjects). In this work, we describe a technique for gene selection and classification of microarray data based on the recently proposed potential support vector machine (P-SVM) for feature selection and a nu-SVM for classification. The P-SVM expands the decision function in terms of a sparse set of "support features". Based on this novel technique for feature selection, we suggest a fully automated method for gene selection, hyper-parameter optimization and microarray classification. Benchmark results are given for the two datasets provided by the ICMLA'08 Automated Micro-Array Classification Challenge.

In this work, a new learning paradigm called target selection is proposed, which can be used to test for associations between a single genetic variable and a multidimensional, quantitative phenotype. In target selection, the task of a learning machine is to chose one out of several nominal target variables, as well as a probabilistic classification function for the selected target. For this new paradigm, a cost function is derived from the concept of mutual information and a learning algorithm is suggested. The significance of the generalization performance of the model learned using target selection is tested using a label permutation test. Here, the proposed target selection paradigm is applied to a genomic imaging study.

The joint analysis of genetic and brain imaging data is the key to understand the genetic underpinnings of brain dysfunctions in several psychiatric diseases known to have a strong genetic component. The goal is to identify associations between genetic and functional or morphometric brain measurements. We here suggest a machine learning method to solve this task, which is based on the recently proposed Potential Support Vector Machine (P-SVM) for variable selection, a subsequent k-NN classification and an estimation of the effect of `correlations by chance'. We apply it to the detection of associations between candidate single nucleotide polymorphisms (SNPs) and volumetric MRI measurements in alcohol dependent patients and healthy controls.

The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this assumption is not valid, because a topo- graphical relationship exists between the objects. In this contribution we consider the special case of image segmentation, where the objects are pixels and where the underlying topography is a 2D regular rectangular grid. We introduce a classification method which not only uses measured vectorial feature information but also the label configuration within a to- pographic neighborhood. Due to the resulting dependence between the labels of neighboring pixels, a collective classification of a set of pixels becomes necessary. We propose a new method called 'Topographic Sup- port Vector Machine' (TSVM), which is based on a topographic kernel and a self-consistent solution to the label assignment shown to be equiv- alent to a recurrent neural network. The performance of the algorithm is compared to a conventional SVM on a cell image segmentation task.

The authors present a new method for the segmentation of autoradiographic brain image stacks, as well as the automatic extraction and 3D visualization of functional information encoded in the image gray values. The difficulty in the evaluation of these so-called "2 1/2 D" data sets is that they do not inherently represent a continuous 3D data volume (as MRI or CT), but consist of a series of consecutive, strongly distorted gray value images of single tissue slices, while the exact position and direction of the original slices varies among different data sets. This complicates the comparison of different experiments. In this work, in addition to providing an enhanced active contour based segmentation algorithm, the concept of "map space" is introduced, which allows the automatic functional evaluation using a shape adaptive, rectangular, normalized, parametric representation of the cortex. Results are presented for a rat brain under acoustical stimulation.