Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionalit...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2018-05, Vol.29 (5), p.1835-1849
Hauptverfasser:	Xuan, Junyu, Lu, Jie, Zhang, Guangquan, Xu, Richard Yi Da, Luo, Xiangfeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayes methods Bivariate analysis Clustering Co-clustering Correlation Data models Factorization Gaussian process Learning systems Matrix decomposition nonnegative matrix factorization Nonparametric statistics Probability distribution functions probability graphical model Sparse matrices Stochastic processes text mining
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!