Learning Transformations for Clustering and Classification

@article{Qiu2015LearningTF,
  title={Learning Transformations for Clustering and Classification},
  author={Qiang Qiu and Guillermo Sapiro},
  journal={Journal of Machine Learning Research},
  year={2015},
  volume={16},
  pages={187-225}
}
A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to their underlying low-dimensional subspaces. However, low-dimensional intrinsic structures are often… CONTINUE READING
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