Multiclass Microarray Data Classification Using SRC Approximations

Sparse representation provides a good discrimination power for different categories of data. This characteristic of sparse representation has led to incredible classification results in many applications. Inspired by the superior performance of the Sparse Representation based Classifier (SRC) and its extensions, we address the problem of tumor classification using some approximations of the SRC algorithm. Microarray data which contain gene expression information of tumors are very high dimensional data. On the other hand, a limited number of microarray samples is usually available. So, conventional classification approaches are in trouble to cope with this problem. Also, one of the main drawbacks of the sparse representation-based methods is the computational complexity of the l1 minimization step. By increasing the size of the data, this problem will be more serious. Accordingly, in this study, we use some of the accelerated proposed sparse coding algorithms. Our experimental results show the remarkable ability of the sparse representation based methods by using the SL0 algorithm as compared to the state of the art tumor classification approaches.