Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation
This work addresses a computational bottleneck in nonnegative low-rank matrix approximation for applications such as data clustering and pattern recognition, offering an incremental improvement over existing methods.
The paper tackles the computational expense of projecting onto fixed-rank matrices in nonnegative low-rank matrix approximation by proposing an alternating projection method that uses tangent spaces to approximate these projections, achieving linear convergence to near-optimal solutions and demonstrating better performance in terms of computational time and accuracy compared to nonnegative matrix factorization methods in applications like data clustering and hyperspectral data analysis.
In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices. In the nonnegative low rank matrix approximation method, the projection onto the manifold of fixed rank matrices can be expensive as the singular value decomposition is required. We propose to use the tangent space of the point in the manifold to approximate the projection onto the manifold in order to reduce the computational cost. We show that the sequence generated by the alternating projections onto the tangent spaces of the fixed rank matrices manifold and the nonnegative matrix manifold, converge linearly to a point in the intersection of the two manifolds where the convergent point is sufficiently close to optimal solutions. This convergence result based inexact projection onto the manifold is new and is not studied in the literature. Numerical examples in data clustering, pattern recognition and hyperspectral data analysis are given to demonstrate that the performance of the proposed method is better than that of nonnegative matrix factorization methods in terms of computational time and accuracy.