Biclustering via mixtures of regression models

• 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
• Institution: Singapore Management University

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.