Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
This addresses clustering challenges in domains like image analysis, but appears incremental as it builds on existing transform-based methods.
The paper tackles the problem of clustering by introducing a method that jointly learns nonlinear transforms with discriminative priors, using a parametric min-max measure for assignment. Numerical experiments on image clustering show advantages over state-of-the-art methods.
This paper presents a novel clustering concept that is based on jointly learned nonlinear transforms (NTs) with priors on the information loss and the discrimination. We introduce a clustering principle that is based on evaluation of a parametric min-max measure for the discriminative prior. The decomposition of the prior measure allows to break down the assignment into two steps. In the first step, we apply NTs to a data point in order to produce candidate NT representations. In the second step, we preform the actual assignment by evaluating the parametric measure over the candidate NT representations. Numerical experiments on image clustering task validate the potential of the proposed approach. The evaluation shows advantages in comparison to the state-of-the-art clustering methods.