Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

@article{Yang2020InversePR,
  title={Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition},
  author={X. Yang and L. Tian and Y. Chen and L. Yang and S. Xu and Wen-Ming Wu},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2020},
  volume={17},
  pages={1262-1275}
}
  • X. Yang, L. Tian, +3 authors Wen-Ming Wu
  • Published 2020
  • Computer Science, Medicine, Biology, Mathematics
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • 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… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 46 REFERENCES
    Metasample-Based Sparse Representation for Tumor Classification
    101
    Robust Classification Method of Tumor Subtype by Using Correlation Filters
    49
    Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation
    13
    Sparse Representation for Classification of Tumors Using Gene Expression Data
    79
    A novel sparse coding algorithm for classification of tumors based on gene expression data
    10
    RPCA-Based Tumor Classification Using Gene Expression Data
    51
    Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
    2604