Unsupervised Deep Embedding for Clustering Analysis

  • Peng X
  • Feng J
  • Lu J
 et al. 
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Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transformation of latent representations that are easy to cluster; but in practice, the transformation from the latent space to the data can be more complicated. In this work, we assume that this transformation is an unknown and possibly nonlinear function. To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN). The motivation is to keep the advantages of jointly optimizing the two tasks, while exploiting the deep neural network's ability to approximate any nonlinear function. This way, the proposed approach can work well for a broad class of generative models. Towards this end, we carefully design the DNN structure and the associated joint optimization criterion, and propose an effective and scalable algorithm to handle the formulated optimization problem. Experiments using five different real datasets are employed to showcase the effectiveness of the proposed approach.

Author-supplied keywords

  • ()
  • Clustering ensemble
  • Factor analysis
  • Fault diagnosis
  • Feature extraction
  • Image clustering
  • Image representation
  • Machine Learning
  • Machine Learning Applications
  • Machine Learning Methods
  • Machine learning
  • Neural networks
  • Non-negative matrix factorization with sparsity an
  • Novel Machine Learning Algorithms Track
  • Semi-supervised
  • Special Track on Machine Learning
  • Sun
  • Support vector machines
  • Training
  • active clustering
  • active k-medoids
  • active learning
  • binary k-means
  • cell
  • centroid-based clustering
  • clustering
  • clustering prior
  • convolutional neural network
  • deep belief network
  • deep learning
  • dimensionality reduction
  • factorial K-means
  • feature
  • feed-forward
  • fuzzy c-means
  • identifiability
  • image clustering
  • image labeling
  • label feature
  • machine learning
  • matrix factorization
  • multi-index hashing
  • multi-modal learning
  • multi-view clustering
  • reduced K-means
  • structure regularizer
  • study
  • subspace learning
  • supervised
  • tensor factorization
  • unsupervised
  • unsupervised feature learning
  • unsupervised learning

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  • Xi Peng

  • Jiashi Feng

  • Jiwen Lu

  • Wei-yun Yau

  • Zhang Yi

  • Sinno Jialin Pan

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