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132
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CVPR
2001
IEEE
16 years 5 months ago
Learning Spatially Localized, Parts-Based Representation
In this paper, we propose a novel method, called local nonnegative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual pat...
Stan Z. Li, XinWen Hou, HongJiang Zhang, QianSheng...
144
Voted
JMLR
2006
175views more  JMLR 2006»
15 years 3 months ago
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
We exploit the biconvex nature of the Euclidean non-negative matrix factorization (NMF) optimization problem to derive optimization schemes based on sequential quadratic and secon...
Matthias Heiler, Christoph Schnörr
169
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ICANNGA
2007
Springer
191views Algorithms» more  ICANNGA 2007»
15 years 10 months ago
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way...
Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Rob...
141
Voted
JMLR
2012
13 years 6 months ago
NIMFA: A Python Library for Nonnegative Matrix Factorization
NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factoriza...
Marinka Zitnik, Blaz Zupan
153
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ISNN
2007
Springer
15 years 9 months ago
Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorization
Nonnegative Matrix and Tensor Factorization (NMF/NTF) and Sparse Component Analysis (SCA) have already found many potential applications, especially in multi-way Blind Source Separ...
Andrzej Cichocki, Rafal Zdunek