Many real life applications brought by modern technologies often have multiple data sources, which are usually characterized by both attributes and pairwise similarities at the same time. For example in webpage ranking, a webpage is usually represented by a vector of term values, and meanwhile the internet linkages induce pairwise similarities among the webpages. Although both attributes and pa...

In this paper, a novel local discriminant embedding method, Discriminant Clustering Embedding (DCE), is proposed for face recognition with image sets. DCE combines the effectiveness of submanifolds, which are extracted by clustering for each subject’s image set, characterizing the inherent structure of face appearance manifold and the discriminant property of discriminant embedding. The low-dim...

Given the nonlinear manifold structure of facial images, a new kernel-based supervised manifold learning algorithm based on locally linear embedding (LLE), called discriminant kernel locally linear embedding (DKLLE), is proposed for facial expression recognition. The proposed DKLLE aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the...

The existing Diffusion Maps method brings diffusion to data samples by Markov random walk. In this paper, to provide a general solution form of Diffusion Maps, first, we propose the generalized single-graph-diffusion embedding framework on the basis of graph embedding framework. Second, by designing the embedding graph of the framework, an algorithm, namely Locally Discriminant Diffusion Projec...

Manifold learning approaches such as locally linear embedding algorithm (LLE) and isometric mapping (Isomap) algorithm are aimed to discover the intrinsical low dimensional variables from high-dimensional nonlinear data. While, in order to achieve effective recognition tasks based on manifold learning, many problems remain to be solved. In this paper, we propose unified algorithm based on LLE a...

To narrow down the gap between low-level visual features and high-level semantic concepts in content-based image retrieval(CBIR) system, a new dimensionality reduction algorithm called tensor biased discriminant Euclidean embedding (TBDEE) is proposed in this paper. The key idea of this algorithm is as follows: First, the image data are represented with high order tensor structure so that the c...

Images can vary according to changes in viewpoint, resolution, noise, and illumination. In this paper, we aim to learn representations for an image, which are robust to wide changes in such environmental conditions, using training pairs of matching and non-matching local image patches that are collected under various environmental conditions. We present a regularized discriminant analysis that ...