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Supervised multivariate learning with simultaneous feature auto-grouping and dimension reduction
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DOI：10.1111/rssb.12492.png)
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摘要
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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 modelling 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, although 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.
Keyword:
clustering
information criterion
low-rank matrix estimation
minimax lower bounds
nonasymptotic statistical analysis
nonconvex optimization
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
OPTIMAL SELECTION OF REDUCED RANK ESTIMATORS OF HIGH-DIMENSIONAL MATRICES高维矩阵降秩估计量的最优选择.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
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