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

Auditory-Nerve

書目名稱Non-negative Matrix Factorization Techniques影響因子(影響力)




書目名稱Non-negative Matrix Factorization Techniques影響因子(影響力)學(xué)科排名




書目名稱Non-negative Matrix Factorization Techniques網(wǎng)絡(luò)公開度




書目名稱Non-negative Matrix Factorization Techniques網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Non-negative Matrix Factorization Techniques被引頻次




書目名稱Non-negative Matrix Factorization Techniques被引頻次學(xué)科排名




書目名稱Non-negative Matrix Factorization Techniques年度引用




書目名稱Non-negative Matrix Factorization Techniques年度引用學(xué)科排名




書目名稱Non-negative Matrix Factorization Techniques讀者反饋




書目名稱Non-negative Matrix Factorization Techniques讀者反饋學(xué)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.

勉勵

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.

場所

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

斷斷續(xù)續(xù)

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