We have witnessed great interest and a wealth of promise in content-based image retrieval
as an emerging technology. While the last decade laid foundation to such promise, it also …

In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA. The …

The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric
used to compute distances between different examples. In this paper, we show how to learn …

We consider learning representations (features) in the setting in which we have access to
multiple unlabeled views of the data for representation learning while only one view is …

In this paper we propose a novel recurrent neural network architecture for video-based
person re-identification. Given the video sequence of a person, features are extracted from …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA and …

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 …

Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …