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418,870
Stratified Regularity Measures with Jensen-Shannon Divergence
"... This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. Jensen-Shannon divergence is used to compute a set-similarity of intensity distributions derived from stratified data. We prove that derived regulari ..."
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This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. Jensen-Shannon divergence is used to compute a set-similarity of intensity distributions derived from stratified data. We prove that derived
Jensen-shannon boosting learning for object recognition
- In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’05
"... In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shannon (JS) divergence [2] into AdaBoost learn-ing. JS divergence is advantageous in that it provides more appropriate measu ..."
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Cited by 22 (0 self)
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In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shannon (JS) divergence [2] into AdaBoost learn-ing. JS divergence is advantageous in that it provides more appropriate
Query Hardness Estimation Using Jensen-Shannon Divergence Among Multiple Scoring Functions
"... Abstract. We consider the issue of query performance, and we propose a novel method for automatically predicting the difficulty of a query. Unlike a number of existing techniques which are based on examining the ranked lists returned in response to perturbed versions of the query with respect to the ..."
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Cited by 23 (3 self)
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by multiple retrieval engines will be relatively similar for “easy ” queries but more diverse for “difficult ” queries. By appropriately employing Jensen-Shannon divergence to measure the “diversity ” of the returned results, we demonstrate a methodology for predicting query difficulty whose performance
Feature Selection for Clustering with Constraints Using Jensen-Shannon Divergence
"... In semi-supervised clustering, domain knowledge can be converted to constraints and used to guide the clustering. In this paper we propose a feature selection algorithm for semi-supervised clustering. In our method, features are conditionally independent. Feature saliency is first computed in unsupe ..."
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in unsupervised clustering using the Expectation Maximization model. Then, it is refined in the Tuning step to minimize the Featurewise Constraint Violation Measure, calculated based on the Jensen-Shannon divergence. Experimental results show that a small amount of supervision can improve the performance
Multiscale Jensen-Shannon Divergence based analysis of beta wave attention EEG
"... Abstract. The article adopted the multiscale Jensen- Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states ..."
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Abstract. The article adopted the multiscale Jensen- Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states
Robust Super-Resolution by Minimizing a Gaussian-weighted L2 Error Norm
"... Abstract. Super-resolution restoration is the problem of restoring a high-resolution scene from multiple degraded low-resolution images under motion. Due to imaging blur and noise, this problem is ill-posed. Additional constraints such as smoothness of the solution via regularization is often requir ..."
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required to obtain a stable solution. While adding a regularization term to the cost function is a standard practice in image restoration, we propose a restoration algorithm that does not require this extra regularization term. The robustness of the algorithm is achieved by a Gaussian-weighted L2-norm
Energy-based Geometric MultiModel Fitting
, 2010
"... Geometric model fitting is a typical chicken-&-egg problem: data points should be clustered based on geometric proximity to models whose unknown parameters must be estimated at the same time. Most existing methods, including generalizations of RANSAC, greedily search for models with most inliers ..."
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Cited by 26 (4 self)
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inliers (within a threshold) ignoring overall classification of points. We formulate geometric multi-model fitting as an optimal labeling problem with a global energy function balancing geometric errors and regularity of inlier clusters. Regularization based on spatial coherence (on some near
c ○ 2000 Kluwer Academic Publishers. Manufactured in The Netherlands. An Analysis of Edge Detection by Using the Jensen-Shannon Divergence
"... Abstract. This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In s ..."
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Abstract. This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation
Article Multiscale Model Selection for High-Frequency Financial Data of a Large Tick Stock by Means of the Jensen–Shannon Metric
"... entropy ..."
Fits
, 2005
"... and especially linear fits, with errors on both axes, extra variance of the data points and other complications ..."
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and especially linear fits, with errors on both axes, extra variance of the data points and other complications
Results 1 - 10
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418,870
