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.
CITATION STYLE
Velu, R., Zhou, Z., & Tee, C. W. (2019). Biclustering via Mixtures of Regression Models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11537 LNCS, pp. 533–549). Springer Verlag. https://doi.org/10.1007/978-3-030-22741-8_38
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