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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Veröffentlicht in:IEEE transactions on signal processing Jg. 65; H. 1; S. 256 - 269
Hauptverfasser: Bo Yang, Xiao Fu, Sidiropoulos, Nicholas D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2017
Schlagworte:
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
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Zusammenfassung: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-dimension representations that are well-suited for the intended task is more appealing. This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low-dimensional representations of matrix and tensor data. The proposed approach leverages matrix and tensor factorization models that produce essentially unique latent representations of the data to unravel latent cluster structure-which is otherwise obscured because of the freedom to apply an oblique transformation in latent space. At the same time, latent cluster structure is used as prior information to enhance the performance of factorization. Specific contributions include several custom-built problem formulations, corresponding algorithms, and discussion of associated convergence properties. Besides extensive simulations, real-world datasets such as Reuters document data and MNIST image data are also employed to showcase the effectiveness of the proposed approaches.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2016.2614491