Journal Article10.1016/J.DSP.2015.03.011
Non-negative tensor factorization models for Bayesian audio processing
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TL;DR: An overview of matrix and tensor factorization methods from a Bayesian perspective, giving emphasis on both the inference methods and modeling techniques, and describes the general statistical framework.
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About: This article is published in Digital Signal Processing. The article was published on 01 Dec 2015. The article focuses on the topics: Tensor (intrinsic definition) & Audio signal processing.
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References
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
14.2K
•Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
- 01 Aug 2006
TL;DR: Looking for competent reading resources?
10.1K
Learning parts of objects by non-negative matrix factorization
D. D. Lee
- 01 Jan 1999
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
9.6K
Analysis of individual differences in multidimensional scaling via an n-way generalization of 'eckart-young' decomposition
J. Douglas Carroll,Jih-Jie Chang +1 more
TL;DR: In this paper, an individual differences model for multidimensional scaling is outlined in which individuals are assumed differentially to weight the several dimensions of a common "psychological space" and a corresponding method of analyzing similarities data is proposed, involving a generalization of Eckart-Young analysis to decomposition of three-way (or higher-way) tables.
5K
Some mathematical notes on three-mode factor analysis
TL;DR: The model for three-mode factor analysis is discussed in terms of newer applications of mathematical processes including a type of matrix process termed the Kronecker product and the definition of combination variables.
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