Transcription of Unsupervised Deep Embedding for Clustering Analysis
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Unsupervised deep Embedding for Clustering AnalysisJunyuan of WashingtonRoss AI Research (FAIR)Ali of WashingtonAbstractClustering is central to many data-driven appli-cation domains and has been studied extensivelyin terms of distance functions and grouping al-gorithms. Relatively little work has focused onlearning representations for Clustering . In thispaper, we propose deep Embedded Clustering (DEC), a method that simultaneously learns fea-ture representations and cluster assignments us-ing deep neural networks. DEC learns a map-ping from the data space to a lower-dimensionalfeature space in which it iteratively optimizes aclustering objective.
Unsupervised Deep Embedding for Clustering Analysis 2011), and REUTERS (Lewis et al.,2004), comparing it with standard and state-of-the-art clustering methods (Nie
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