# Deep Kernel Learning for Clustering

## TL;DR

This paper introduces a deep learning method for creating custom kernels that improve clustering accuracy, offering faster training and better out-of-sample performance compared to existing methods.

## Contribution

It presents a novel neural network-based kernel learning approach optimized with the Hilbert Schmidt Information Criterion, outperforming traditional and deep clustering techniques.

## Key findings

- Outperforms state-of-the-art deep clustering methods
- Faster training due to gradient-based optimization
- Effective on both real-life and synthetic datasets

## Abstract

We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--and are at least as expressive as--spectral clustering. Our training objective, based on the Hilbert Schmidt Information Criterion, can be optimized via gradient adaptations on the Stiefel manifold, leading to significant acceleration over spectral methods relying on eigendecompositions. Finally, our trained embedding can be directly applied to out-of-sample data. We show experimentally that our approach outperforms several state-of-the-art deep clustering methods, as well as traditional approaches such as $k$-means and spectral clustering over a broad array of real-life and synthetic datasets.