CoOL clusters data by training neural networks via expectation-maximization gradients to assign groups based on agreement.
Unsupervised Deep Embedding for Clustering Analysis
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
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.
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2026 2representative citing papers
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Cohort Organized Learning: Clustering Through Agreement
CoOL clusters data by training neural networks via expectation-maximization gradients to assign groups based on agreement.
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