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.
with k-means and then projecting the data into a lower di-mensions where the inter-cluster variance is maximized. This process is repeated in EM-style iterations until conver-gence. However, this framework is limited to linear embed-ding; our method employs deep neural networks to perform non-linear embedding that is necessary for more complex ...
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