A-Optimal Non-negative Projection for image representation
As a central problem in computer vision and pattern recognition, data representation has attracted great attention in the past years. Non-negative matrix factorization (NMF) which is a useful data representation method makes great contribution on finding the latent structure of the data and leads to a parts-based representation by decomposing the data matrix into a few bases and encodings with nonnegative constraints. However, non-negative constraint is insufficient for getting more robust data representation. In this paper, we propose a novel method, called A-Optimal Non-negative Projection (ANP) for image data representation and further analysis. ANP imposes a constraint on the encoding factor as a regularizer during matrix factorization. In this way, the learned data representation leads to a stable linear model no matter what kind of data label is selected for further processing. Thus, it can preserve more intrinsic characteristics of the data regardless of any specific labels. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.