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 …

The explosive growth in volume, velocity, and diversity of data produced by mobile devices
and cloud applications has contributed to the abundance of data or 'big data.'Available …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

How closely related are two nodes in a graph? How to compute this score quickly, on huge,
disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance …

Continuous Integration (CI) has become a best practice of modern software development.
Yet, at present, we have a shortfall of insight into the testing practices that are common in CI …

Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the
distance metric for the input data patterns. Distance Metric learning is to learn a distance …

Distance metric is a key issue in many machine learning algorithms. This paper considers a
general problem of learning from pairwise constraints in the form of must-links and cannot …

This paper introduces a web image search reranking approach that explores multiple
modalities in a graph-based learning scheme. Different from the conventional methods that …

We present a new framework for multimedia content analysis and retrieval which consists of
two independent algorithms. First, we propose a new semi-supervised algorithm called …

Distance metric learning is a fundamental problem in data mining and knowledge discovery.
Many representative data mining algorithms, such as k k-nearest neighbor classifier …