Hyperspectral images are one of the most important fundamental and strategic information resources, imaging the same ground object with hundreds of spectral bands varying from the ultraviolet to the microwave. With the emergence of huge volumes of high-resolution hyperspectral images produced by all sorts of imaging sensors, processing and analysis of these images requires effective retrieval t...

Consider the classical coupon-collector's problem in which items of m distinct types arrive in sequence. An arriving item is installed in system i > 1 if i is the smallest index such that system i does not contain an item of the arrival's type. We study the expected number of items in system j at the moment when system 1 first contains an item of each type.

Many similarity measures in multimedia databases and decision-support systems are based on underlying vector spaces of high dimensionality. Data-partitioning index methods for such spaces (for example, grid les, R-trees, and their variants) generally work well for low-dimensional spaces, but perform poorly as dimensionality increases. This problem has become known as thèdimensional curse'. This...

Tensor based dimensionality reduction has recently been extensively studied for computer vision applications. To our knowledge, however, there exist no rigorous error analysis on these methods. Here we provide the first error analysis of these methods and provide error bound results similar to Eckart-Young Theorem which plays critical role in the development and application of singular value de...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...

Dimensionality reduction aims at representing high-dimensional data in low-dimensional spaces, mainly for visualization and exploratory purposes. As an alternative to projections on linear subspaces, nonlinear dimensionality reduction, also known as manifold learning, can provide data representations that preserve structural properties such as pairwise distances or local neighborhoods. Very rec...

This paper presents a new approach to reduce the dimensionality of Phone Log likelihood Ratio (PLLR) features, which have been shown to be effective for language recognition, by removing the likelihoods corresponding to less frequent phonemes. In this work, phoneme frequencies are estimated using a suitable phoneme recogniser. Following this, an i-vector framework is used to represent the total...

Dimensionality reduction is an important problem in pattern recognition. I n a speech recognition system, the size of the feature set is normally large in the order of 40. Therefore, it is necessary to reduce the dimensionality of the feature space for efficient and effective speech recognition. Two popular methods to reduce the dimensionality of the feature space are Linear Discriminat Analysi...

The Wiener index of a graph Gis defined as W(G) =1/2[Sum(d(i,j)] over all pair of elements of V(G), where V (G) is the set of vertices of G and d(i, j) is the distance between vertices i and j. In this paper, we give an algorithm by GAP program that can be compute the Wiener index for any graph also we compute the Wiener index of HAC5C7[p, q] and HAC5C6C7[p, q] nanotubes by this program.