Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering

Dimensionality reduction techniques play an essential role in data analytics, signal processing, and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimensio...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 65; no. 1; pp. 256 - 269
Main Authors:	Bo Yang, Xiao Fu, Sidiropoulos, Nicholas D.
Format:	Journal Article
Language:	English
Published:	IEEE 01.01.2017
Subjects:	Algorithm design and analysis Analytical models clustering Clustering algorithms clustering prior Convergence Data models dimensionality reduction Factor analysis factorial K -means identifiability matrix factorization reduced K -means Signal processing subspace learning Tensile stress tensor factorization
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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