Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
This work addresses dense problems in supervised multivariate learning for statisticians, offering an interpretable alternative to low-rank modeling and variable selection methods.
The paper tackles the challenge of supervised multivariate learning with dense coefficient problems by proposing a clustered reduced-rank learning (CRL) framework that automatically groups features for improved interpretability and relaxes sparsity assumptions, demonstrating statistical accuracy and interpretability through simulations and real-data experiments.
Modern high-dimensional methods often adopt the "bet on sparsity" principle, while in supervised multivariate learning statisticians may face "dense" problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modeling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, though not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.