The covariance matrix in the Mahalanobis distance can be trained by semi-definite programming, but training for a large size data set is inefficient. In this paper, we constrain the covariance matrix to be diagonal and train Mahalanobis kernels by linear programming (LP). Training can be formulated by ν-LP SVMs (support vector machines) or regular LP SVMs. We clarify the dependence of the solut...

This paper presents a Devnagari Numerical recognition method based on statistical discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes, image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance ar...

Local feature matching is an essential component of many image retrieval algorithms. Euclidean and Mahalanobis distances are mostly used in order to compare two feature vectors. The first distance does not give satisfactory results in many cases and is inappropriate in the typical case where the components of the feature vector are incommensurable, whereas the second one requires training data....

We discuss an approach to change detection in digital remotely sensed imagery that relies on the Fuzzy Post Classification Comparison technique. We use the fuzzy -means classifier together with the Mahalanobis distance as the basis for a metric of class membership for a individual pixel. We note that the value of the fuzzy exponent in a fuzzy classifier is based on the ratios of the reciprocals...

Searching and organizing growing digital music collections requires automatic classification of music. This paper describes a new system, tested on the task of artist identification, that uses support vector machines to classify songs based on features calculated over their entire lengths. Since support vector machines are exemplarbased classifiers, training on and classifying entire songs inst...

The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed BOOSTMETRIC, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used t...

In this paper a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptical clustering is accomplished by incorporating the Mahalanobis distance measure into the learning rules, and underutilization of smaller clusters is avoided by incorporating a frequency-sensitive term. In the paper, an ...

In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT’S) which can interoperate between them. However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime. This paper proposes a novel ranking algorithm, which combines multi attribute d...

In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT’S) which can interoperate between them. However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime. This paper proposes a novel ranking algorithm, which combines multi attribute d...

Outlier detection methods are fundamental to all of data analysis. They are desirably robust, affine invariant, and computationally easy in any dimension. The powerful projection pursuit approach yields the “projection outlyingness”, which is affine invariant and highly robust and does not impose ellipsoidal contours like the Mahalanobis distance approach. However, it is highly computationally ...