Stable Sparse Subspace Embedding for Dimensionality Reduction

@article{Chen2020StableSS,
  title={Stable Sparse Subspace Embedding for Dimensionality Reduction},
  author={Li Chen and Shuizheng Zhou and Jiajun Ma},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.02844}
}
  • Li Chen, Shuizheng Zhou, Jiajun Ma
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • 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
    1 Citations

    References

    SHOWING 1-10 OF 27 REFERENCES
    Random projection in dimensionality reduction: applications to image and text data
    • 1,231
    • PDF
    Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
    • 222
    • PDF
    Recovering the Optimal Solution by Dual Random Projection
    • 55
    • PDF
    Sparse principal component analysis via regularized low rank matrix approximation
    • 556
    • PDF
    Very sparse random projections
    • 484
    • PDF
    Low-Rank Approximation and Regression in Input Sparsity Time
    • 249
    • PDF
    Face recognition experiments with random projection
    • 179
    • PDF
    Random Projections for $k$-means Clustering
    • 114
    • PDF
    Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification
    • 92
    • PDF
    K-means clustering using random matrix sparsification
    • 4
    • PDF