Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

In social settings, individuals interact through webs of relationships. Each individual is a
node in a complex network (or graph) of interdependencies and generates data, lots of data …

Deep learning algorithms seek to exploit the unknown structure in the input distribution in
order to discover good representations, often at multiple levels, with higher-level learned …

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional
representation of a dataset that lends itself to a clustering interpretation. It is possible that the …

Theoretical results suggest that in order to learn the kind of complicated functions that can
represent high-level abstractions (eg, in vision, language, and other AI-level tasks), one may …

We consider the general problem of learning from labeled and unlabeled data, which is
often called semi-supervised learning or transductive in-ference. A principled approach to …

An approach to semi-supervised learning is proposed that is based on a Gaussian random
field model. Labeled and unlabeled data are represented as vertices in a weighted graph …

We propose a family of learning algorithms based on a new form of regularization that
allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised …

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents
an accessible treatment of the two main components of support vector machines (SVMs) …

We propose an appearance-based face recognition method called the Laplacianface
approach. By using locality preserving projections (LPP), the face images are mapped into a …