# Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment

## TL;DR

This paper introduces a novel clustering method that jointly learns nonlinear transforms with priors, using a min-max measure for discriminative assignment, demonstrating improved performance on image clustering tasks.

## Contribution

It proposes a new clustering framework based on jointly learned nonlinear transforms and a min-max discriminative measure, enhancing clustering accuracy.

## Key findings

- Outperforms state-of-the-art clustering methods on image datasets
- Demonstrates the effectiveness of jointly learned nonlinear transforms
- Validates the approach through numerical experiments

## Abstract

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.