Unsupervised Deep Embedding for Clustering Analysis
This addresses the challenge of clustering in data-driven domains by introducing a novel method that integrates representation learning, offering potential benefits for applications in image and text analysis.
The paper tackles the problem of learning representations for clustering by proposing Deep Embedded Clustering (DEC), which simultaneously learns feature representations and cluster assignments using deep neural networks, resulting in significant improvement over state-of-the-art methods on image and text corpora.
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.