Scalable Metric Learning via Weighted Approximate Rank Component Analysis

@inproceedings{Jose2016ScalableML,
  title={Scalable Metric Learning via Weighted Approximate Rank Component Analysis},
  author={Cijo Jose and François Fleuret},
  booktitle={ECCV},
  year={2016}
}
We are interested in the large-scale learning of Mahalanobis distances, with a particular focus on person re-identification. We propose a metric learning formulation called Weighted Approximate Rank Component Analysis (WARCA). WARCA optimizes the precision at top ranks by combining the WARP loss with a regularizer that favors orthonormal linear mappings, and avoids rank-deficient embeddings. Using this new regularizer allows us to adapt the large-scale WSABIE procedure and to leverage the Adam… CONTINUE READING
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