Subspace-Constrained Quadratic Matrix Factorization: Algorithm and Applications

TL;DR

This paper introduces a subspace-constrained quadratic matrix factorization model for manifold learning, jointly capturing tangent and normal spaces, with proven convergence and superior performance on synthetic and real data.

Contribution

The paper proposes a novel quadratic matrix factorization model with subspace constraints, along with an alternating minimization algorithm and theoretical analysis of its properties.

Findings

Outperforms existing methods in capturing low-dimensional structures

Demonstrates robustness and efficacy on synthetic and real datasets

Provides convergence guarantees for the proposed algorithm

Abstract

Matrix Factorization has emerged as a widely adopted framework for modeling data exhibiting low-rank structures. To address challenges in manifold learning, this paper presents a subspace-constrained quadratic matrix factorization model. The model is designed to jointly learn key low-dimensional structures, including the tangent space, the normal subspace, and the quadratic form that links the tangent space to a low-dimensional representation. We solve the proposed factorization model using an alternating minimization method, involving an in-depth investigation of nonlinear regression and projection subproblems. Theoretical properties of the quadratic projection problem and convergence characteristics of the alternating strategy are also investigated. To validate our approach, we conduct numerical experiments on synthetic and real-world datasets. Results demonstrate that our model outperforms existing methods, highlighting its robustness and efficacy in capturing core low-dimensional structures.