Learning Deep Representations By Distributed Random Samplings

Xiao-Lei Zhang

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arXiv · 1312.4405

Learning Deep Representations By Distributed Random Samplings

Xiao-Lei Zhang

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Abstract

In this paper, we propose an extremely simple deep model for the unsupervised nonlinear dimensionality reduction -- deep distributed random samplings, which performs like a stack of unsupervised bootstrap aggregating. First, its network structure is novel: each layer of the network is a group of mutually independent $k$-centers clusterings. Second, its learning method is extremely simple: the $k$ centers of each clustering are only $k$ randomly selected examples from the training data; for small-scale data sets, the $k$ centers are further randomly reconstructed by a simple cyclic-shift operation. Experimental results on nonlinear dimensionality reduction show that the proposed method can learn abstract representations on both large-scale and small-scale problems, and meanwhile is much faster than deep neural networks on large-scale problems.

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BibTeX RIS

Xiao-Lei Zhang. 2013-12-16. Learning Deep Representations By Distributed Random Samplings. https://arxiv.org/abs/1312.4405

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