Online Learning in The Manifold of Low-Rank Matrices

@inproceedings{Shalit2010OnlineLI,
  title={Online Learning in The Manifold of Low-Rank Matrices},
  author={Uri Shalit and Daphna Weinshall and Gal Chechik},
  booktitle={NIPS},
  year={2010}
}
When learning models that are represented in matrix forms, en forci g a low-rank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, n aive approaches for minimizing functions over the set of low-rank matrices are e ither prohibitively time consuming (repeated singular value decomposition of t he matrix) or numerically unstable (optimizing a factored representation of the low rank matrix). We build on recent… CONTINUE READING
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