Biclustering via mixtures of regression models

Biclustering via mixtures of regression models

Biclustering of observations and the variables is of interest in many scientific disciplines. In a single set of data matrix it is handled through the singular value decomposition. Here we deal with two sets of variables: response and predictor sets. We model the joint relationship via regression mo...

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Bibliographic Details
Main Authors: VELU, Raja, ZHOU, Zhaoque, TEE, Chyng Wen
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
multivariate regression
singular value decomposition
dimension reduction
mixture models
Finance and Financial Management
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/6405
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7404/viewcontent/Velu__Zhou__and_Tee__2019______Biclustering_via_Mixtures_of_Regression_Models.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Biclustering of observations and the variables is of interest in many scientific disciplines. In a single set of data matrix it is handled through the singular value decomposition. Here we deal with two sets of variables: response and predictor sets. We model the joint relationship via regression models and then apply SVD on the coefficient matrix. The sparseness condition is introduced via Group Lasso. The approach discussed here is quite general and is illustrated with an example from Finance.

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