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. Also, we derive a kernelized version which
allows to take advantage of state-of-the-art features for re-identification
when data-set size permits kernel computation.
Benchmarks on recent and standard re-identification data-sets show that our
method beats existing state-of-the-art techniques both in term of accuracy and
speed. We also provide experimental analysis to shade lights on the properties
of the regularizer we use, and how it improves performance.

我们提出了一种名为带权近似秩分量分析（WARCA）的度量学习公式，它通过将 WARP 损失与偏爱正交线性映射和避免秩缺乏嵌入的正则化器相结合，优化顶级排名的精度，我们的方法在最近和标准的重新识别数据集上都超越了现有的最先进技术，不仅在准确性上优于现有的技术，而且在速度上也表现出极佳的表现。