Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

TL;DR

This paper introduces a joint deep learning and clustering method that learns a nonlinear transformation to produce clustering-friendly latent spaces, improving clustering performance over traditional sequential approaches.

Contribution

It proposes a novel joint optimization framework combining deep neural networks with K-means clustering to handle complex nonlinear transformations from latent space to data.

Findings

The method effectively recovers clustering-friendly representations.

It outperforms traditional sequential DR and clustering approaches.

The approach is scalable and adaptable to various datasets.

Abstract

Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transformation of latent representations that are easy to cluster; but in practice, the transformation from the latent space to the data can be more complicated. In this work, we assume that this transformation is an unknown and possibly nonlinear function. To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN). The motivation is to keep the advantages of jointly optimizing the two tasks, while exploiting the deep neural network's ability to approximate any nonlinear function. This way, the proposed approach can work well for a broad class of generative models. Towards this end, we carefully design the DNN structure and the associated joint optimization criterion, and propose an effective and scalable algorithm to handle the formulated optimization problem. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.