Scalable Distributed Nonnegative Matrix Factorization with Block-Wise Updates
نویسندگان
چکیده
منابع مشابه
Scalable Nonnegative Matrix Factorization with Block-wise Updates
Nonnegative Matrix Factorization (NMF) has been applied with great success to many applications. As NMF is applied to massive datasets such as web-scale dyadic data, it is desirable to leverage a cluster of machines to speed up the factorization. However, it is challenging to efficiently implement NMF in a distributed environment. In this paper, we show that by leveraging a new form of update f...
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Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of data, naturally the samples are collected and stored distributively in local computational nodes. Thus, there is a growing need to develop algorithms in a distributed memory architecture. We propose a novel distributed a...
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In Nonnegative Matrix Factorization (NMF), a nonnegative matrix is approximated by a product of lower-rank factorizing matrices. Quadratic Nonnegative Matrix Factorization (QNMF) is a new class of NMF methods where some factorizing matrices occur twice in the approximation. QNMF finds its applications in graph partition, bi-clustering, graph matching, etc. However, the original QNMF algorithms ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2018
ISSN: 1041-4347
DOI: 10.1109/tkde.2017.2785326