A Constrained Algorithm Based NMFα for Image Representation

Abstract EN

Nonnegative matrix factorization (NMF) is a useful tool in learning a basic representation of image data.

However, its performance and applicability in real scenarios are limited because of the lack of image information.

In this paper, we propose a constrained matrix decomposition algorithm for image representation which contains parameters associated with the characteristics of image data sets.

Particularly, we impose label information as additional hard constraints to the α-divergence-NMF unsupervised learning algorithm.

The resulted algorithm is derived by using Karush-Kuhn-Tucker (KKT) conditions as well as the projected gradient and its monotonic local convergence is proved by using auxiliary functions.

In addition, we provide a method to select the parameters to our semisupervised matrix decomposition algorithm in the experiment.

Compared with the state-of-the-art approaches, our method with the parameters has the best classification accuracy on three image data sets.