To overcome the problem of invariant pattern recognition, Simard, LeCun, and Denker (1993) proposed a successful nearest-neighbor approach based on tangent distance, attaining state-of-the-art accuracy. Since this approach needs great computational and memory effort, Hastie, Simard, and Säckinger (1995) proposed an algorithm (HSS) based on singular value decomposition (SVD), for the generation ...

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor,...

Node similarity is a fundamental problem in graph analytics. However, node similarity between nodes in different graphs (inter-graph nodes) has not received a lot of attention yet. The inter-graph node similarity is important in learning a new graph based on the knowledge of an existing graph (transfer learning on graphs) and has applications in biological, communication, and social networks. I...

The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification r...

This paper examines the problem of database organization and retrieval based on computing metric pairwise distances. A low-dimensional Euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional Euclidean space suffers from the “curse of dimensionality”. Thus, techniques designed for searching metric spaces must be used. We evaluate several su...

Hashing is one of the effective techniques for fast Approximate Nearest Neighbour (ANN) search. Traditional single-bit quantization (SBQ) in most hashing methods incurs lots of quantization error which seriously degrades the search performance. To address the limitation of SBQ, researchers have proposed promising multi-bit quantization (MBQ) methods to quantize each projection dimension with mu...

A probabilistic scheme is presented for simulating evolution of polycrystalline microstructures during deformation. Microstructure images are described using a compact descriptor called the nearest–neighbor conditional orientation correlation function, defined as the probability density of occurrence of a crystal orientation at one pixel distance from a known orientation. The neighborhood infor...

Since thematic classes are represented with high spectral variance, pixel-based classifications generally result with incontinuous and inhomogeneous outputs. Objectbased classifications overcome this problem by the approach similar to human seeing and interpreting activity. First, image is segmented into smaller objects, and then image objects are assigned to classes according to their spectral...

Why do employers discriminate against job applicants who reside in poor, distant neighborhoods? Previous research indicates that employers call back applicants from these neighborhoods at lower rates, but the motivation for employer discrimination based on residential neighborhood remains unclear. Employers could be responding to long commuting distances, which could lead to higher employee abs...