Corpus ID: 209500864

Fast Generalized Matrix Regression with Applications in Machine Learning

  title={Fast Generalized Matrix Regression with Applications in Machine Learning},
  author={Haishan Ye and Shusen Wang and Zhihua Zhang and Tong Zhang},
  • Haishan Ye, Shusen Wang, +1 author Tong Zhang
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • Fast matrix algorithms have become the fundamental tools of machine learning in big data era. The generalized matrix regression problem is widely used in the matrix approximation such as CUR decomposition, kernel matrix approximation, and stream singular value decomposition (SVD), etc. In this paper, we propose a fast generalized matrix regression algorithm (Fast GMR) which utilizes sketching technique to solve the GMR problem efficiently. Given error parameter $0<\epsilon<1$, the Fast GMR… CONTINUE READING

    Figures, Tables, and Topics from this paper.


    Improved Approximation Algorithms for Large Matrices via Random Projections
    • Tamás Sarlós
    • Mathematics, Computer Science
    • 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06)
    • 2006
    • 598
    • Highly Influential
    Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
    • 19
    • PDF
    Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
    • 2,488
    • Highly Influential
    • PDF
    Relative-Error CUR Matrix Decompositions
    • 364
    • PDF
    Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
    • 222
    • PDF
    Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling
    • 138
    • PDF
    Fast approximation of matrix coherence and statistical leverage
    • 349
    • Highly Influential
    • PDF
    SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
    • 24
    • PDF
    Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
    • 367
    • Highly Influential
    • PDF
    Randomized Algorithms for Matrices and Data
    • M. Mahoney
    • Computer Science
    • Found. Trends Mach. Learn.
    • 2011
    • 676
    • PDF