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The Bhattacharyya Measure
, 2008
"... An important problem in computer vision is measuring the dissimilarity between distributions of features, such as colour and texture (cf. (Rubner, Puzicha, Tomasi & Buhmann, 2001)). The focus of this note is on the Bhattacharyya measure and its derivatives. For a discussion of the statistical ..."
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An important problem in computer vision is measuring the dissimilarity between distributions of features, such as colour and texture (cf. (Rubner, Puzicha, Tomasi & Buhmann, 2001)). The focus of this note is on the Bhattacharyya measure and its derivatives. For a discussion of the statistical
Bhattacharyya Boosting
"... In this paper, we discuss a new feature selection criterion in boosting. Our method directly optimizes Bhattacharyya distance between the weighted positive and negative samples to find the feature vector instead of bruteforce selection from a feature pool. Unlike some similar work including FisherB ..."
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Cited by 1 (0 self)
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In this paper, we discuss a new feature selection criterion in boosting. Our method directly optimizes Bhattacharyya distance between the weighted positive and negative samples to find the feature vector instead of bruteforce selection from a feature pool. Unlike some similar work including Fisher
Malay Bhattacharyya
"... The topology of many reallife networks, e.g., the world wide web, social networks, ecological networks, genetic networks, comply with scalefree models. In scalefree models, the vertices of the underlying graph follow a powerlaw degree distribution. Observably, the graphs corresponding to most of ..."
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The topology of many reallife networks, e.g., the world wide web, social networks, ecological networks, genetic networks, comply with scalefree models. In scalefree models, the vertices of the underlying graph follow a powerlaw degree distribution. Observably, the graphs corresponding to most of the reallife networks are fuzzy in nature. An important problem of knowledge engineering that has evolved in various reallife networks is to identify the largest group of similar vertices in such networks that are functionally associated. Here, the problem of finding the largest group or association of vertices that are dense (denoted as dense Nvertexlet) in a fuzzy scalefree graph is addressed. Density quantifies the degree of similarity within a group of vertices in a graph. The density of an Nvertexlet is defined in a novel way that ensures significant participation of all the vertices in the Nvertexlet. First, it is established that the problem is NPcomplete in nature. An upper bound on the size of the largest dense Nvertexlet in a fuzzy graph, with respect to certain density threshold value, is then derived. Finally, an O(n2 log n), n being the number of vertices in the graph, heuristic graph mining algorithm that
Bhattacharyya and Expected Likelihood Kernels
 In Conference on Learning Theory
, 2003
"... We introduce a new class of kernels between distributions. These induce a kernel on the input... ..."
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Cited by 37 (2 self)
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We introduce a new class of kernels between distributions. These induce a kernel on the input...
The BurbeaRao and Bhattacharyya centroids
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2011
"... We study the centroid with respect to the class of informationtheoretic BurbeaRao divergences that generalize the celebrated JensenShannon divergence by measuring the nonnegative Jensen difference induced by a strictly convex and differentiable function. Although those BurbeaRao divergences are ..."
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Cited by 26 (14 self)
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We study the centroid with respect to the class of informationtheoretic BurbeaRao divergences that generalize the celebrated JensenShannon divergence by measuring the nonnegative Jensen difference induced by a strictly convex and differentiable function. Although those BurbeaRao divergences
RESULTS RELATED TO THE BHATTACHARYYA MATRICES
"... SUMMARY. In this paper, we consider characterizations based on the Bhattacharyya matrices. We begin the paper by obtaining the structure of the rth moment of the random variable X about the origin for a natural exponential family when the variance is a polynomial of the mean such that the mean is a ..."
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SUMMARY. In this paper, we consider characterizations based on the Bhattacharyya matrices. We begin the paper by obtaining the structure of the rth moment of the random variable X about the origin for a natural exponential family when the variance is a polynomial of the mean such that the mean is a
Phone Clustering Using The Bhattacharyya Distance
 In Proceedings of the International Conference on Spoken Language Processing
, 1996
"... In this paper we study using the classificationbased Bhattacharyya distance measure to guide biphone clustering. The Bhattacharyya distance is a theoretical distance measure between two Gaussian distributions which is equivalent to an upper bound on the optimal Bayesian classification error probabi ..."
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Cited by 26 (4 self)
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In this paper we study using the classificationbased Bhattacharyya distance measure to guide biphone clustering. The Bhattacharyya distance is a theoretical distance measure between two Gaussian distributions which is equivalent to an upper bound on the optimal Bayesian classification error
The bhattacharyya metric as an absolute similarity measure for frequency coded data
 Kybernetika
, 1997
"... A recurring problem that arises throughout the sciences is that of deciding whether two statistical distributions differ or are consistent currently the chisquared statistic is the most commonly used technique for addressing this problem. This paper explains the drawbacks of the chisquared statis ..."
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Cited by 77 (5 self)
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of the use of the Bhattacharyya measure as an upper bound on misclassification in a twoclass problem. The affinity between the Bhattacharyya and Matusita measures is described and we show that the measure is applicable to any distribution of data. We explain that the Bhattacharyya measure is consistent
Feature Selection based on the Bhattacharyya Distance
"... This paper presents a Bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multiclass classification problem. In our sche ..."
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This paper presents a Bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multiclass classification problem. In our
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