Randomness and Sparsity Induced Codebook Learning with Application to Cancer Image Classification

Abstract

Codebook learning is one of the central research topics in computer vision and machine learning. In this paper, we propose a new codebook learning algorithm, Randomized Forest Sparse Coding (RFSC), by harvesting the following three concepts: (1) ensemble learning, (2) divide-and-conquer, and (3) sparse coding. Given a set of training data, a randomized tree can be used to perform data partition (divide-and-conquer); after a tree is built, a number of bases are learned from the data within each leaf node for a sparse representation (subspace learning via sparse coding); multiple trees with diversities are trained (ensemble), and the collection of bases of these trees constitute the codebook. These three concepts in our codebook learning algorithm have the same target but with different emphasis: subspace learning via sparse coding makes a compact representation, and reduces the information loss; the divide-and-conquer process efficiently obtains the local data clusters; an ensemble of diverse trees provides additional robustness. We have conducted classification experiments on cancer images as well as a variety of natural image datasets and the experiment results demonstrate the efficiency and effectiveness of the proposed method.