Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

TL;DR

This paper introduces a novel stable inverse projection representation classification method that effectively utilizes test samples and enhances tumor recognition accuracy using microarray gene expression data.

Contribution

It proposes the inverse projection representation and category contribution rate, improving robustness and stability in tumor classification tasks.

Findings

Outperforms existing methods on six tumor datasets

Effectively uses test samples for classification

Provides a stable and robust tumor recognition framework

Abstract

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.