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 …

The metric learning problem is concerned with learning a distance function tuned to a
particular task, and has been shown to be useful when used in conjunction with nearest …

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

The main theme of this paper is to develop a novel eigenvalue optimization framework for
learning a Mahalanobis metric. Within this context, we introduce a novel metric learning …

The world's corpora of data grow in size and complexity every day, making it increasingly
difficult for experts to make sense out of their data. Although machine learning offers …

We study the problem of learning local metrics for nearest neighbor classification. Most
previous works on local metric learning learn a number of local unrelated metrics. While …

Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …

Deep models with multiple layers have demonstrated their potential in learning abstract and
invariant features for better representation and classification of remote sensing images …

Recently, metric learning and similarity learning have attracted a large amount of interest.
Many models and optimization algorithms have been proposed. However, there is relatively …

In this paper, an efficient similarity measure method is proposed for printed circuit board
(PCB) surface defect detection. The advantage of the presented approach is that the …