Corpus ID: 237532095

Separating Boundary Points via Structural Regularization for Very Compact Clusters

  title={Separating Boundary Points via Structural Regularization for Very Compact Clusters},
  author={Xin Ma and Won Hwa Kim},
  • Xin Ma, Won Hwa Kim
  • Published 9 June 2021
  • Computer Science
Clustering algorithms have significantly improved along with Deep Neural Networks which provide effective representation of data. Existing methods are built upon deep autoencoder and self-training process that leverages the distribution of cluster assignments of samples. However, as the fundamental objective of the autoencoder is focused on efficient data reconstruction, the learnt space may be sub-optimal for clustering. Moreover, it requires highly effective codes (i.e., representation) of… Expand

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