Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with $\ell_0$-constraints

• URL: http://arxiv.org/abs/2309.09239v1
• Date: Sun, 17 Sep 2023 11:05:08 GMT
• Title: Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with $\ell_0$-constraints
• Authors: Weifeng Yang and Wenwen Min
• Abstract summary: Multilinear logistic regression serves as a powerful tool for the analysis of multidimensional data. We propose an Accelerated Proximal Alternating Minim-MLSR model to solve the $ell_0$-MLSR. We also demonstrate that APALM$+$ is globally convergent to a first-order critical point as well as to establish convergence by using the Kurdy-Lojasiewicz property.
• Score: 2.323238724742687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tensor data represents a multidimensional array. Regression methods based on low-rank tensor decomposition leverage structural information to reduce the parameter count. Multilinear logistic regression serves as a powerful tool for the analysis of multidimensional data. To improve its efficacy and interpretability, we present a Multilinear Sparse Logistic Regression model with $\ell_0$-constraints ($\ell_0$-MLSR). In contrast to the $\ell_1$-norm and $\ell_2$-norm, the $\ell_0$-norm constraint is better suited for feature selection. However, due to its nonconvex and nonsmooth properties, solving it is challenging and convergence guarantees are lacking. Additionally, the multilinear operation in $\ell_0$-MLSR also brings non-convexity. To tackle these challenges, we propose an Accelerated Proximal Alternating Linearized Minimization with Adaptive Momentum (APALM$^+$) method to solve the $\ell_0$-MLSR model. We provide a proof that APALM$^+$ can ensure the convergence of the objective function of $\ell_0$-MLSR. We also demonstrate that APALM$^+$ is globally convergent to a first-order critical point as well as establish convergence rate by using the Kurdyka-Lojasiewicz property. Empirical results obtained from synthetic and real-world datasets validate the superior performance of our algorithm in terms of both accuracy and speed compared to other state-of-the-art methods.

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