Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

In computer vision and pattern recognition researches, the studied objects are often
characterized by multiple feature representations with high dimensionality, thus it is …

Real-world datasets often involve multiple views of data items, eg, a Web page can be
described by both its content and anchor texts of hyperlinks leading to it; photos in Flickr …

We propose a novel algorithm for supervised dimensionality reduction named manifold
partition discriminant analysis (MPDA). It aims to find a linear embedding space where the …

No single feature can satisfactorily characterize the semantic concepts of an image.
Multiview learning aims to unify different kinds of features to produce a consensual and …

We study discrete geodesic foliations of surfaces---foliations whose leaves are all
approximately geodesic curves---and develop several new variational algorithms for …

Cross media retrieval systems have received increasing interest in recent years. Due to the
semantic gap between low-level features and high-level semantic concepts of multimedia …

In many data analysis tasks, one is often confronted with very high dimensional data. The
manifold assumption, which states that the data is sampled from a submanifold embedded in …

Manifold learning offers nonlinear dimensionality reduction of high-dimensional datasets. In
this paper, we bring geometry processing to bear on manifold learning by introducing a new …

Nowadays, most social Websites allow users to annotate resources (such as Web pages
and images) with keywords, ie tags. Collaborative tagging data reflects the semantic …