Deep $k$-Means: Jointly clustering with $k$-Means and learning representations
This addresses the challenge of enhancing clustering accuracy for data analysis tasks, though it appears incremental as it builds on prior work in joint clustering and representation learning.
The paper tackles the problem of jointly clustering and learning representations to improve clustering performance, proposing a continuous reparametrization of the k-Means objective function that leads to a joint solution, with efficacy demonstrated on various datasets.
We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for $k$-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.