Scalable Metric Learning via Weighted Approximate Rank Component Analysis

Understand

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 stochastic optimization algorithm, which results in an algorithm that scales gracefully to very large data-sets.