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JMLR
2012
11 years 8 months ago
Primal-Dual methods for sparse constrained matrix completion
We develop scalable algorithms for regular and non-negative matrix completion. In particular, we base the methods on trace-norm regularization that induces a low rank predicted ma...
Yu Xin, Tommi Jaakkola
ICANNGA
2007
Springer
191views Algorithms» more  ICANNGA 2007»
13 years 12 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...
ECML
2005
Springer
13 years 7 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
ICASSP
2011
IEEE
12 years 9 months ago
An iterative least-squares technique for dereverberation
Some recent dereverberation approaches that have been effective for automatic speech recognition (ASR) applications, model reverberation as a linear convolution operation in the s...
Kshitiz Kumar, Bhiksha Raj, Rita Singh, Richard M....
KDD
2012
ACM
212views Data Mining» more  KDD 2012»
11 years 8 months ago
Fast bregman divergence NMF using taylor expansion and coordinate descent
Non-negative matrix factorization (NMF) provides a lower rank approximation of a matrix. Due to nonnegativity imposed on the factors, it gives a latent structure that is often mor...
Liangda Li, Guy Lebanon, Haesun Park