2012 LargeScaleDistributedNonNegativ

• (Sindhwani & Ghoting, 2012) ⇒ Vikas Sindhwani, and Amol Ghoting. (2012). “Large-scale Distributed Non-negative Sparse Coding and Sparse Dictionary Learning.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339610

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Large-scale+Distributed+Non-negative+Sparse+Coding+and+Sparse+Dictionary+Learning
• http://dl.acm.org/citation.cfm?id=2339530.2339610&preflayout=flat#citedby

Quotes

Author Keywords

• Dictionary learning; learning; mapreduce; nmf; sparse coding

Abstract

We consider the problem of building compact, unsupervised representations of large, high-dimensional, non-negative data using sparse coding and dictionary learning schemes, with an emphasis on executing the algorithm in a Map-Reduce environment. The proposed algorithms may be seen as parallel optimization procedures for constructing sparse non-negative factorizations of large, sparse matrices. Our approach alternates between a parallel sparse coding phase implemented using greedy or convex (<i l₁) regularized risk minimization procedures, and a sequential dictionary learning phase where we solve a set of l₀ optimization problems exactly. These two-fold sparsity constraints lead to better statistical performance on text analysis tasks and at the same time make it possible to implement each iteration in a single Map-Reduce job. We detail our implementations and optimizations that lead to the ability to factor matrices with more than 100 million rows and billions of non-zero entries in just a few hours on a small commodity cluster.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 LargeScaleDistributedNonNegativ	Amol Ghoting Vikas Sindhwani			Large-scale Distributed Non-negative Sparse Coding and Sparse Dictionary Learning				10.1145/2339530.2339610		2012