Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering

@article{Yang2017LearningFH,
  title={Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering},
  author={Bo Yang and Xiao Fu and Nikos D. Sidiropoulos},
  journal={IEEE Transactions on Signal Processing},
  year={2017},
  volume={65},
  pages={256-269}
}
Dimensionality reduction techniques play an essential role in data analytics, signal processing, and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low… CONTINUE READING
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