This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological variations. Technical variations originate from sample preparation, fixation, and staining from multiple laboratories, whereas biological variations originate from tissue content. Image patches are represented with invariant features at local and global scales, where local refers to responses measured with Laplacian of Gaussians, and global refers to measurements in the color space. Experiments are designed to learn dictionaries through sparse coding, and to train classifiers through kernel methods using normal, necrotic, apoptotic, and tumor regions with characteristics of high cellularity. Two different kernel methods, that of a support vector machine (SVM) and a kernel discriminant analysis (KDA), were used for comparative analysis. Preliminary investigation on the histological samples of Glioblastoma multiforme (GBM) indicates the kernel methods perform as good, if not better, than sparse coding with redundant representation.