Similarity measurement is a critical component in content-based image retrieval systems,
and learning a good distance metric can significantly improve retrieval performance …

This paper proposes an efficient sparse metric learning algorithm in high dimensional space
via an l 1-penalized log-determinant regularization. Compare to the most existing distance …

Intraclass compactness and interclass separability are crucial indicators to measure the
effectiveness of a model to produce discriminative features, where intraclass compactness …

Unlike other biometric authentication methods, gait recognition is noninvasive and effective
from a distance. However, the performance of gait recognition will suffer in the low-resolution …

This paper focuses on develo** a new framework of kernelizing Mahalanobis distance
learners. The new KPCA trick framework offers several practical advantages over the …

Supervised dimensionality reduction with tensor representation has attracted great interest
in recent years. It has been successfully applied to problems with tensor data, such as image …

In many real-world applications, such as image retrieval, it would be natural to measure the
distances from one instance to others using instance specific distance which captures the …

Mahalanobis distance metric takes feature weights and correlation into account in the
distance computation, which can improve the performance of many similarity/dissimilarity …

We present a general framework of semi-supervised dimensionality reduction for manifold
learning which naturally generalizes existing supervised and unsupervised learning …

Several dimensionality reduction methods based on the max-min idea have been proposed
in recent years and can obtain good classification performance. In this paper, inspired by the …