RLS-Based On-Line Sparse Nonnegative Matrix Factorization Method for Acoustic Signal Processing Systems

2013 ◽  
Vol E96.A (5) ◽  
pp. 980-985 ◽  
Author(s):  
Seokjin LEE
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Factorization Method ◽  
Signal Processing Systems ◽  
Sparse Nonnegative Matrix Factorization ◽  
On Line

scholarly journals Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangguang Dai ◽  
Chuandong Li ◽  
Biqun Xiang
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimal Solution ◽  
Factorization Method ◽  
Real World Data ◽  
Factorization Problem ◽  
Sparse Nonnegative Matrix Factorization ◽  
And Performance

We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect the intrinsic graph structure and provide the sparse representation. Different from some existing traditional methods, the inertial neural network was developed, which can be used to optimize our proposed matrix factorization problem. By adopting one parameter in the neural network, the global optimal solution can be searched. Finally, simulations on numerical examples and clustering in real-world data illustrate the effectiveness and performance of the proposed method.

scholarly journals Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 354
Author(s):  
Jing Zhou
Keyword(s):  
Feature Extraction ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Recognition Rate ◽  
Nonnegative Matrix ◽  
Facial Feature ◽  
Face Database ◽  
Base Matrix ◽  
Sparse Nonnegative Matrix Factorization ◽  
Pca Method

Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.

scholarly journals Sparse Nonnegative Matrix Factorization Strategy for Cochlear Implants

2015 ◽  
Vol 19 ◽  
pp. 233121651561694 ◽  
Author(s):  
Hongmei Hu ◽  
Mark E. Lutman ◽  
Stephan D. Ewert ◽  
Guoping Li ◽  
Stefan Bleeck
Keyword(s):  
Cochlear Implants ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Sparse Nonnegative Matrix Factorization
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