The KNN algorithm is one of the most popular data mining algorithms. It has been widely
and successfully applied to data analysis applications across a variety of research topics in …

For multimode processes, one generally establishes local monitoring models corresponding
to local modes. However, the significant features of previous modes may be catastrophically …

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis
(MMRA) for exclusively dealing with the long-standing “worst-case class separation” …

Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …

There are still many challenges to be resolved in the task of ship classification in synthetic
aperture radar (SAR) images, such as limited number of labeled samples in SAR domain …

Distance metric learning aims to deal with the data distribution by learning a suitable
distance metric from the training instances. For distance metric learning, the optimization …

Distance metric learning has been a promising technology to improve the performance of
algorithms related to distance metrics. The existing distance metric learning methods are …

Fine-grained ship classification in synthetic aperture radar (SAR) images is a challenging
task, since SAR images can only provide limited discriminative information due to the …

Metric learning has attracted significant attention due to its high effectiveness and efficiency
for pattern recognition task. Traditional supervised metric learning algorithms attempt to seek …

Metric learning is a branch of machine learning that aims to learn from the given training
data a valid distance metric, with which the similarity between samples can be more …