Fast sparse representation approaches for the classification of high-dimensional biological data

Classifying genomic and proteomic data is very important to predict diseases in a very early stage and investigate signaling pathways. However, this poses many computationally challenging problems, such as curse of dimensionality, noise, redundancy and so on. The principle of sparse representation has been applied to analyzing high-dimensional biological data within the frameworks of clustering, classification, and dimension reduction approaches. However, the existing sparse representation approaches are either inefficient or have the difficulty of kernelization. In this paper, we propose fast active-set-based sparse coding approach and a dictionary learning framework for classifying high-dimensional biological data. We show that they can be easily kernelized. Experimental results show that our approaches are very efficient, and satisfactory accuracy can be obtained compared with existing approaches.