Stable Sparse Subspace Embedding for Dimensionality Reduction

  title={Stable Sparse Subspace Embedding for Dimensionality Reduction},
  author={Li Chen and Shuizheng Zhou and Jiajun Ma},
  journal={Knowl. Based Syst.},
  • Li Chen, Shuizheng Zhou, Jiajun Ma
  • Published 2020
  • Computer Science, Mathematics
  • Knowl. Based Syst.
  • Abstract Sparse random projection (RP) is a popular tool for dimensionality reduction that shows promising performance with low computational complexity. However, in the existing sparse RP matrices, the positions of non-zero entries are usually randomly selected. Although they adopt uniform sampling with replacement, due to large sampling variance, the number of non-zeros is uneven among rows of the projection matrix which is generated in one trial, and more data information may be lost after… CONTINUE READING


    Publications referenced by this paper.
    Random projection in dimensionality reduction: applications to image and text data
    • 1,217
    • PDF
    Sparse principal component analysis via regularized low rank matrix approximation
    • 552
    • PDF
    Very sparse random projections
    • 476
    • PDF
    Sparse Principal Component Analysis
    • 2,275
    • PDF
    Low-Rank Approximation and Regression in Input Sparsity Time
    • 235
    • PDF
    Database-friendly random projections: Johnson-Lindenstrauss with binary coins
    • 1,059
    • Highly Influential
    • PDF
    Face recognition experiments with random projection
    • 179
    • PDF
    Random projection-based multiplicative data perturbation for privacy preserving distributed data mining
    • 527
    • PDF
    Random Projections for $k$-means Clustering
    • 111
    • PDF