Log In Sign Up

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

by   Xiao-Hui Yang, et al.

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.


page 8

page 12

page 14


Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation

Microarray gene expression data-based breast tumor classification is an ...

Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification

Sparse representation-based classification (SRC) has attracted much atte...

Integrative analysis of gene expression and phenotype data

The linking genotype to phenotype is the fundamental aim of modern genet...

Gene selection for cancer classification using a hybrid of univariate and multivariate feature selection methods

Various approaches to gene selection for cancer classification based on ...

Biogeography-Based Informative Gene Selection and Cancer Classification Using SVM and Random Forests

Microarray cancer gene expression data comprise of very high dimensions....

Optimal Estimation of Simultaneous Signals Using Absolute Inner Product with Applications to Integrative Genomics

Integrating the summary statistics from genome-wide association study (G...