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» Descent methods for Nonnegative Matrix Factorization
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CVPR
2010
IEEE
13 years 7 months ago
Sparse representation using nonnegative curds and whey
It has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer...
Yanan Liu, Fei Wu, Zhihua Zhang, Yueting Zhuang, S...
CVPR
2009
IEEE
13 years 10 months ago
Nonlinear Nonnegative Component Analysis
In this paper general solutions for Nonlinear Nonnegative Component Analysis for data representation and recognition are proposed. That is, motivated by a combination of the Nonne...
Stefanos Zafeiriou, Maria Petrou
ECML
2005
Springer
13 years 9 months ago
Clustering and Metaclustering with Nonnegative Matrix Decompositions
Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm ...
Liviu Badea
ICANNGA
2007
Springer
191views Algorithms» more  ICANNGA 2007»
14 years 1 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...
CIKM
2010
Springer
13 years 6 months ago
Yes we can: simplex volume maximization for descriptive web-scale matrix factorization
Matrix factorization methods are among the most common techniques for detecting latent components in data. Popular examples include the Singular Value Decomposition or Nonnegative...
Christian Thurau, Kristian Kersting, Christian Bau...