Abstract:
Million-point large-scale subspace clustering (LS 2 C) is examined in this article. Despite being state-of-the-art for small-scale data points, many subspace clustering methods cannot directly solve the LS 2 C problem. These methods use all data points as a dictionary to build huge coding models, which increases time and space complexity. This article presents a learnable subspace clustering paradigm to efficiently solve the LS 2 C problem. Instead of computationally intensive classical coding models, learn a parametric function to partition high-dimensional subspaces into low-dimensional ones. An alternating minimization algorithm can solve the parametric function using a unified, robust, predictive coding machine (RPCM). We also perform parametric function bounded contraction analysis. This article is the first to successfully cluster millions of data points using subspace clustering methods. Our paradigm outperforms state-of-the-art methods on million-scale data sets.
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