Online Categorical Subspace Learning for Sketching Big Data with Misses

  title={Online Categorical Subspace Learning for Sketching Big Data with Misses},
  author={Yanning Shen and Morteza Mardani and Georgios B. Giannakis},
  journal={IEEE Transactions on Signal Processing},
With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical observations, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, this paper develops a novel categorical subspace learning approach to unravel the latent structure for… CONTINUE READING


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