A Novel Approach for Stable Selection of Informative Redundant Features from High Dimensional fMRI Data

Numerous functional imaging studies have reported neural activities during the experience of specific emotions or cognitive activities and demonstrated the potentials of functional imaging MRI for the classification of cognitive states or identification of mental disorders. In this paper, we consider learning from fMRI data as a pattern recognition problem and mainly focus on how to accurately and stably identify the relevant features (either voxels or network connections) that participate in a given cognitive task or that are closely related with certain mental disorders. In this paper, we will mainly consider the binary classification problems such as discriminating patients of certain mental discorder from the normal persons or classifying different cognative states, though the proposed idea can also be extended to the case of regression As we know, with the rapid development of data capture and storage technologies, the "curse of dimensionality" becomes a common issue in many fields [1] including the field of pattern recognition and machine learning, where "curse of dimensionality" often refers to an extremely high dimensional feature space. Therefore, feature selection, as a way of dimensional reduction, is critical in many pattern recognition applications such as medical image analysis, computer vision, speech recognition and many more [2]. In this paper, we consider the related challeges in the neuroimaging data based pattern recognition, where besides the "curse of dimensionality", feature selection has another common difficulty, which lies in the small number of training samples, due to varied reasons.