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Online and batch learning of pseudo-metrics (2004)

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by Shai Shalev-shwartz , Yoram Singer , Andrew Y. Ng
Venue:In ICML
Citations:40 - 4 self
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BibTeX

@INPROCEEDINGS{Shalev-shwartz04onlineand,
    author = {Shai Shalev-shwartz and Yoram Singer and Andrew Y. Ng},
    title = {Online and batch learning of pseudo-metrics},
    booktitle = {In ICML},
    year = {2004},
    pages = {743--750},
    publisher = {ACM Press}
}

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Abstract

We describe and analyze an online algorithm for supervised learning of pseudo-metrics. The algorithm receives pairs of instances and predicts their similarity according to a pseudo-metric. The pseudo-metrics we use are quadratic forms parameterized by positive semi-definite matrices. The core of the algorithm is an update rule that is based on successive projections onto the positive semi-definite cone and onto half-space constraints imposed by the examples. We describe an efficient procedure for performing these projections, derive a worst case mistake bound on the similarity predictions, and discuss a dual version of the algorithm in which it is simple to incorporate kernel operators. The online algorithm also serves as a building block for deriving a large-margin batch algorithm. We demonstrate the merits of the proposed approach by conducting experiments on MNIST dataset and on document filtering. 1.

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11 Matrix computations - Loan - 1989
1 Adjustment Learning and Relevant Component Analysis. 7th Euro. conf - Shental, Hertz, et al. - 2002
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