Results 41 to 50 of about 7,842 (187)
Modulus of convexity for operator convex functions [PDF]
, 2014 Given an operator convex function $f(x)$, we obtain an operator-valued lower bound for $cf(x) + (1-c)f(y) - f(cx + (1-c)y)$, $c \in [0,1]$. The lower bound is expressed in terms of the matrix Bregman divergence.
Kim, Isaac H.
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Adaptive mixture methods based on Bregman divergences [PDF]
Digital Signal Processing, 2013 We investigate adaptive mixture methods that linearly combine outputs of $m$ constituent filters running in parallel to model a desired signal. We use "Bregman divergences" and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints.
Donmez, M. A., Inan, H. A., Kozat, S. S.
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Regularised transfer learning for hyperspectral image classification
IET Computer Vision, 2019 This study presents a transfer learning method for addressing the insufficient sample problem in hyperspectral image classification. In order to find common feature representation for both the source domain and target domain, we introduce a ...
Qian Shi +3 more
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CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio ...
X. Wu +3 more
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Beta-Divergence as a Subclass of Bregman Divergence [PDF]
IEEE Signal Processing Letters, 2011 In this paper, we present a complete proof that the β-divergence is a particular case of Bregman divergence. This little-known result makes it possible to straightforwardly apply theorems about Bregman divergences to β-divergences. This is of interest for numerous applications since these divergences are widely used, for instance in non-negative matrix
Romain Hennequin +2 more
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Sum decomposition of divergence into three divergences
, 2018 Divergence functions play a key role as to measure the discrepancy between two points in the field of machine learning, statistics and signal processing. Well-known divergences are the Bregman divergences, the Jensen divergences and the f-divergences. In
Nishiyama, Tomohiro
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Extropy: Complementary Dual of Entropy [PDF]
, 2015 This article provides a completion to theories of information based on entropy, resolving a longstanding question in its axiomatization as proposed by Shannon and pursued by Jaynes.
Agrò, Gianna +2 more
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Machine Learning and Knowledge ExtractionThe purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures.
Tuyen Pham +2 more
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Centroid-Based Clustering with ab-Divergences [PDF]
, 2019 Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use.
Cruces Álvarez, Sergio Antonio +3 more
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An advanced level set method based on Bregman divergence for inhomogeneous image segmentation [PDF]
, 2017 © 2017 IEEE. Intensity inhomogeneity often occurs in real images. Local information based level set methods are comparatively effective in segmenting image with inhomogeneous intensity. However, in practice, these models suffer from local minima and high
Shi, D., Tian, Feng, Zhang, Y., Zhu, M.
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