The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

Since about 100 years ago, to learn the intrinsic structure of data, many representation
learning approaches have been proposed, either linear or nonlinear, either supervised or …

Similarity between objects plays an important role in both human cognitive processes and
artificial systems for recognition and categorization. How to appropriately measure such …

Recent years have witnessed the explosive growth of community-contributed images with
rich context information, which is beneficial to the task of image retrieval. It can help us to …

Metric learning has attracted increasing attention due to its critical role in image analysis and
classification. Conventional metric learning always assumes that the training and test data …

In this paper, we address the semisupervised distance metric learning problem and its
applications in classification and image retrieval. First, we formulate a semisupervised …

Soft subspace clustering algorithms have been successfully used for high dimensional data
in recent years. However, the existing algorithms often utilize only one distance function to …

Metric learning has attracted significant attention in the past decades, because of its
appealing advances in various real-world tasks, eg, person re-identification and face …

Metric learning has attracted increasing attentions recently, because of its promising
performance in many visual analysis applications. General supervised metric learning …

Currently, high dimensional data processing confronts two main difficulties: inefficient
similarity measure and high computational complexity in both time and memory space …