Selecting causal brain features with a single conditional independence test per feature

Neural Information Processing Systems

We propose a constraint-based causal feature selection method for identifying causes of a given target variable, selecting from a set of candidate variables, while there can also be hidden variables acting as common causes with the target. We prove that if we observe a cause for each candidate cause, then a single conditional independence test with one conditioning variable is sufficient to decide whether a candidate associated with the target is indeed causing it. We thus improve upon existing methods by significantly simplifying statistical testing and requiring a weaker version of causal faithfulness. Our main assumption is inspired by neuroscience paradigms where the activity of a single neuron is considered to be also caused by its own previous state. We demonstrate successful application of our method to simulated, as well as encephalographic data of twenty-one participants, recorded in Max Planck Institute for intelligent Systems.


Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis

Neural Information Processing Systems

In this study, we present a method for estimating the mutual information for a localized pattern of fMRI data. We show that taking a multivariate information approach to voxel selection leads to a decoding accuracy that surpasses an univariate inforamtion approach and other standard voxel selection methods. Furthermore,we extend the multivariate mutual information theory to measure the functional connectivity between distributed brain regions. By jointly estimating the information shared by two sets of voxels we can reliably map out the connectivities in the human brain during experiment conditions. The multivariate information analysis is able to find strong information flow between PPA and RSC, which confirms existing neuroscience studies on scenes.


Classifiers Fusion for EEG Signals Processing in Human-Computer Interface Systems

AAAI Conferences

In this paper we study the effectiveness of using multiple classifier combination for EEG signals classification aiming to obtain more accurate results than it possible from single classifier system. The developed system employs different features vectors fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, fuzzy integral, majority vote, decision template and some other types of combination methods have been investigated. The ensemble classification task is completed by feeding the Support Vectors Machines with Redial Basis Kernel functions classifiers with different features extracted from the EEG signal for imagination of right and left hands movements (i.e., at EEG channels C3 and C4). The parameters of SVM classifiers were optimized by genetic algorithm. The results show that using classifier fusion methods improved the overall classification performance.


Parallel Feature Selection Inspired by Group Testing

Neural Information Processing Systems

This paper presents a parallel feature selection method for classification that scales up to very high dimensions and large data sizes. Our original method is inspired by group testing theory, under which the feature selection procedure consists of a collection of randomized tests to be performed in parallel. Each test corresponds to a subset of features, for which a scoring function may be applied to measure the relevance of the features in a classification task. We develop a general theory providing sufficient conditions under which true features are guaranteed to be correctly identified. Superior performance of our method is demonstrated on a challenging relation extraction task from a very large data set that have both redundant features and sample size in the order of millions.


Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

Neural Information Processing Systems

Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint ℓ2,1-norm minimization on both loss function and regularization. The ℓ2,1-norm based loss function is robust to outliers in data points and the ℓ2,1-norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence.