Subspace-Constrained Quadratic Matrix Factorization: Algorithm and Applications
This addresses manifold learning problems for data analysis applications, representing an incremental improvement with a novel hybrid method.
The paper tackles the challenge of learning low-dimensional structures in manifold learning by proposing a subspace-constrained quadratic matrix factorization model that jointly learns tangent spaces, normal subspaces, and quadratic forms. Results show it outperforms existing methods on synthetic and real-world datasets, demonstrating robustness and efficacy.
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.