Distributed geometric nonnegative matrix factorization and hierarchical alternating least squares-based nonnegative tensor factorization with the MapReduce paradigm

2018 ◽  
Vol 30 (17) ◽  
pp. e4473 ◽  
Author(s):  
Rafał Zdunek ◽  
Krzysztof Fonał
Keyword(s):  
Least Squares ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Alternating Least Squares ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
Mapreduce Paradigm

NONNEGATIVE TENSOR FACTORIZATION FOR CONTINUOUS EEG CLASSIFICATION

2007 ◽  
Vol 17 (04) ◽  
pp. 305-317 ◽  
Author(s):  
HYEKYOUNG LEE ◽  
YONG-DEOK KIM ◽  
ANDRZEJ CICHOCKI ◽  
SEUNGJIN CHOI
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Viterbi Algorithm ◽  
Nonnegative Matrix ◽  
Data Sets ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Eeg Classification ◽  
Nonnegative Tensor Factorization ◽  
Continuous Eeg

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

scholarly journals An Accelerated Symmetric Nonnegative Matrix Factorization Algorithm Using Extrapolation

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1187
Author(s):  
Peitao Wang ◽  
Zhaoshui He ◽  
Jun Lu ◽  
Beihai Tan ◽  
YuLei Bai ◽  
...  
Keyword(s):  
Real World ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Low Rank ◽  
Tensor Factorization ◽  
Real World Data ◽  
Restart Strategy ◽  
Extrapolation Scheme ◽  
Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) approximates a symmetric nonnegative matrix by the product of a nonnegative low-rank matrix and its transpose. SNMF has been successfully used in many real-world applications such as clustering. In this paper, we propose an accelerated variant of the multiplicative update (MU) algorithm of He et al. designed to solve the SNMF problem. The accelerated algorithm is derived by using the extrapolation scheme of Nesterov and a restart strategy. The extrapolation scheme plays a leading role in accelerating the MU algorithm of He et al. and the restart strategy ensures that the objective function of SNMF is monotonically decreasing. We apply the accelerated algorithm to clustering problems and symmetric nonnegative tensor factorization (SNTF). The experiment results on both synthetic and real-world data show that it is more than four times faster than the MU algorithm of He et al. and performs favorably compared to recent state-of-the-art algorithms.

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