BACKGROUND Physical activity (PA) has numerous health benefits, but older adults live mostly sedentary lifestyles. The physical and social neighborhood environment may encourage/dissuade PA. In particular, neighborhood crime may lead to feeling unsafe and affect older adults' willingness to be physically active. Yet, research on this topic is still inconclusive. Older population, probably the a...

Manifold learning based techniques have been found to be useful for feature space transformations and semi-supervised learning in speech processing. However, the immense computational requirements in building neighborhood graphs have hindered the application of these techniques to large speech corpora. This paper presents an approach for fast computation of neighborhood graphs in the context of...

Nonsmooth analysis of a broad class of functions taking the form F (x) = mini fi(x), where each fi is a convex function. One element of this class of functions is the distance function, which measures the distance between a point and the nearest point on the nearest obstacle. Many motion planning algorithms are based on the distance function, and thus rigorous analysis of the distance function ...

Fuzzy clustering techniques handle the fuzzy relationships among the data points and with the cluster centers (may be termed as cluster fuzziness). On the other hand, distance measures are important to compute the load of such fuzziness. These are the two important parameters governing the quality of the clusters and the run time. Visualization of multidimensional data clusters into lower dimen...

Connectivity is a fundamental concept that is widely utilized in spatial ecology. The majority of connectivity measures used in the recent ecological literature only consider the nearest neighbor patch/population, or patches within a limited neighborhood of the focal patch (a buffer). Meta-analysis suggests that studies using nearest neighbor connectivity measures are much less likely to find s...

Local algorithms for non-linear dimensionality reduction [1], [2], [3], [4], [5] and semi-supervised learning algorithms [6], [7] use spectral decomposition based on a nearest neighborhood graph. In the presence of shortcuts (union of two points whose distance measure along the submanifold is actually large), the resulting embbeding will be unsatisfactory. This paper proposes an algorithm to co...

This paper analyzes if a multi-scale representation of geographical context based on statistical aggregates computed for individualized neighborhoods can lead to improved estimates of neighborhood effect. Our study group consists of individuals born in 1980 that have lived in Sweden since 1995 and we analyze the effect of neighborhood context at age 15 on educational outcome at age 30 controlli...

This paper presents a novel distance concept, Vector-Distance (VD) for high dimensional data. VD extends traditional scalar-distance to a vector-like fashion by collecting multi partial distances from diverse angles. These partial distances are derived from random projections, and they preserve individual features of dimensions as much as possible. Based on VD definition, a method family for ne...

Various kinds of k-nearest neighbor (KNN) based classification methods are the bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice. Essentially, the challenge is to detect the neighborhood of various data sets, while utterly ignorant of the data characteristic. This article introduces a new supervised clas...

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders.Neuromuscular diseases changes, the shape and characteristics of the motor unit action potentials (MUAPs). The MUAPs detected from myopathic patients are characterized by high frequency contents, low peak-to-peak amplitude and MUAPs neuropathic patients a...