Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent MethodsDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Abstract: With the increase of data volume and data dimension, sparse representation learning attracts more and more attention. For high-dimensional data, randomized block coordinate descent methods perform well because they do not need to calculate the gradient along the whole dimension. Existing hard thresholding algorithms evaluate gradients followed by a hard thresholding operation to update the model parameter, which leads to slow convergence. To address this issue, we propose a novel hard thresholding algorithm, called Semi-stochastic Block Coordinate Descent Hard Thresholding Pursuit (SBCD-HTP). Moreover, we present its sparse and asynchronous parallel variants. We theoretically analyze the convergence properties of our algorithms, which show that they have a significantly lower hard thresholding complexity than existing algorithms. Our empirical evaluations on real-world datasets and face recognition tasks demonstrate the superior performance of our algorithms for sparsity-constrained optimization problems.
  • Keywords: Sparse learning, Hard thresholding, High-dimensional regression
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