This paper proposes a new gait recognition method using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is first applied to 1D time-varying distance signals derived from a sequence of silhouette images to reduce it’s dimensionality. Then, LDA is performed to optimize the pattern class ificovtion..And,Spatiotemporal Correlation (STC) and Normalized Euclidean Distan...

A k nearest neighbor (kNN) classifier classifies a query instance to the most frequent class of its k nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and gen...

Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discriminant analysis to estimate an effective metric for computing neighborhoods. We determine the local decision b...

Recently, a distance function was defined on the face-centered cubic and body-centered cubic grids by combining weights and neighborhood sequences. These distances share many properties with traditional path-based distance functions, such as the city-block distance, but are less rotational dependent. We introduce four different error functions which are used to find the optimal weights and neig...

Proximity graphs have been widely used in the area of computational geometry; through a vicinity concept these graphs establish relations of similarity between elements of a set. In this paper we propose the use of Relative Neighborhood Graph (RNG) in metric spaces in order to efficiently answer similarity queries. Additionally we introduce a new algorithm for range queries and nearest neighbor...

Proximity graphs have been widely used in the area of computational geometry; through a vicinity concept these graphs establish relations of similarity between elements of a set. In this paper we propose the use of Relative Neighborhood Graph (RNG) in metric spaces in order to efficiently answer similarity queries. Additionally we introduce a new algorithm for range queries and nearest neighbor...

BACKGROUND The relationship between residential proximity to roadway and long-term survival after acute myocardial infarction (AMI) is unknown. We investigated the association between distance from residence and major roadway and 10-year all-cause mortality after AMI in the Determinants of Myocardial Infarction Onset Study (Onset Study), hypothesizing that living closer to a major roadway at th...

Background—The relationship between residential proximity to roadway and long-term survival after acute myocardial infarction (AMI) is unknown. We investigated the association between distance from residence and major roadway and 10-year all-cause mortality after AMI in the Determinants of Myocardial Infarction Onset Study (Onset Study), hypothesizing that living closer to a major roadway at th...

The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To in...