Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Xiaohui Yang, Li Tian, Yunmei Chen, Lijun Yang, Shuang Xu, Wenming Wu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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 first 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.

Original languageEnglish
Article number8580408
Pages (from-to)1262-1275
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume17
Issue number4
DOIs
StatePublished - 1 Jul 2020
Externally publishedYes

Keywords

  • Tumor classification, inverse projection representation, category contribution rate, classification stability index
  • two-stage hybrid gene selection

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