Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets

2006 ◽  
pp. 254-261 ◽  
Author(s):  
K. Stadlthanner ◽  
F. J. Theis ◽  
E. W. Lang ◽  
A. M. Tomé ◽  
C. G. Puntonet ◽  
...  
Keyword(s):  
Microarray Data ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Data Sets ◽  
Sparse Nonnegative Matrix Factorization

scholarly journals Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Tang ◽  
Linyao Kang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Collaborative Filtering ◽  
Processing Speed ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Experimental Results ◽  
Computational Time ◽  
Data Sets ◽  
Heterogeneous Cluster ◽  
The Matrix

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy.

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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