Titlebook: Non-negative Matrix Factorization Techniques; Advances in Theory a Ganesh R. Naik Book 2016 Springer-Verlag Berlin Heidelberg 2016 Blind So

Auditory-Nerve

书目名称Non-negative Matrix Factorization Techniques影响因子(影响力)




书目名称Non-negative Matrix Factorization Techniques影响因子(影响力)学科排名




书目名称Non-negative Matrix Factorization Techniques网络公开度




书目名称Non-negative Matrix Factorization Techniques网络公开度学科排名




书目名称Non-negative Matrix Factorization Techniques被引频次




书目名称Non-negative Matrix Factorization Techniques被引频次学科排名




书目名称Non-negative Matrix Factorization Techniques年度引用




书目名称Non-negative Matrix Factorization Techniques年度引用学科排名




书目名称Non-negative Matrix Factorization Techniques读者反馈




书目名称Non-negative Matrix Factorization Techniques读者反馈学科排名

Dedication

Graduated

Automatic Extractive Multi-document Summarization Based on Archetypal Analysis, sentences and therefore leads to variability and diversity in content of the generated summaries. We conducted experiments on the data of document understanding conference. Experimental results evidence the improvement of the proposed approach over other closely related methods including ones using the NMF.

勉励

Time-Scale-Based Segmentation for Degraded PCG Signals Using NMF,rences calculated along the time-scales. The simulation results using real recorded noisy PCG data that provide promising performance with high overall accuracy on the segmentation of narrowly separated, high noisy signals by our proposed method.

场所

Nonnegative Matrix Factorizations for Intelligent Data Analysis,ill the understandability requirement in several ways. We also describe a novel method to decompose data into user-defined—hence understandable—parts by means of a mask on the feature matrix, and show the method’s effectiveness through some numerical examples.

Diastole

断断续续

Bounded Matrix Low Rank Approximation,rld datasets illustrate that the proposed method BMA outperforms the state-of-the-art algorithms for recommender system such as stochastic gradient descent, alternating least squares with regularization, SVD++ and bias-SVD on real-world datasets such as Jester, Movielens, Book crossing, Online dating, and Netflix.

CLAM

Highbrow

粘