Locality preserving projection (LPP) is a manifold learning method widely used in pattern
recognition and computer vision. The face recognition application of LPP is known to suffer …

This is a tutorial and survey paper for nonlinear dimensionality and feature extraction
methods which are based on the Laplacian of graph of data. We first introduce adjacency …

Graph-based technique is widely used in projection, clustering, and classification tasks. In
this article, we propose a novel and solid framework, named unsupervised projection with …

As a well-known nonlinear dimensionality reduction method, Laplacian Eigenmaps (LE)
aims to find low dimensional representations of the original high dimensional data by …

Monitoring activities using video data is an important surveillance problem. A special
scenario is to learn the pattern of normal activities and detect abnormal events from a very …

This paper presents an invariant image watermarking scheme by introducing the Polar
Harmonic Transform (PHT), which is a recently developed orthogonal moment method …

Learning graph embeddings for high-dimensional data is an important technology for
dimensionality reduction. The learning process is expected to preserve the discriminative …

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure
simplification of the probabilistic neural network (PNN). Three algorithms are introduced …

This paper proposes a novel tree kernel-based method with rich syntactic and semantic
information for the extraction of semantic relations between named entities. With a parse tree …

Many feature extraction methods reduce the dimensionality of data based on the input graph
matrix. The graph construction which reflects relationships among raw data points is crucial …