J. Lin. Divergence measures based on the Shannon entropy. IEEE Trans. Inform. Theory, 37(1): 145--151, 1991.  Home/Search Document Not in Database Summary Related Articles Check |
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Kernel-Based Object Tracking - Comaniciu, Ramesh, Meer (2003) (16 citations) (Correct)
.... the distance between two discrete distributions as dy ############################ 6 where we chose ##y## ppy; qq ################ y 7 the sample estimate of the Bhattacharyya coefficient between p and q [43] The Bhattacharyya coefficient is a divergence type measure [49] which has a straightforward geometric interpretation. It is the cosine of the angle between the m dimensional unit vectors . The fact that p and q are distributions is thus explicitly taken into account by representing them on the unit hypersphere. At the same time, wecan interpret ....
J. Lin, "Divergence Measures Based on the Shannon Entropy," IEEE Trans. Information Theory, vol. 37, pp. 145-151, 1991.
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....mean of the bandwidth matrices computed at x H #1 h 3 where the weights jH jH j . D. Comaniciu is with the Real Time Vision and Modeling Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540. E mail: comanici scr.siemens.com. Manuscript received 18 Mar. 2002; revised 19 July 2002; accepted 25 July 2002. Recommended for acceptance by S. Sclaroff. For information on obtaining reprints of this article, please send e mail to: tpami computer.org, and reference IEEECS Log Number 116109. 1. The terms bandwidth and scale will be considered ....
....(12) the sparse data needs attention. The local sample size should be sufficiently large for inference. The approach we take is based on the Effective Sample Size [10] which computes the kernel weighted count of the number of points in each window ESSx;H i1 KH KH 0 #0 20 ; 0 ; 18 Using the binomial rule of thumb, we cancel the inference when ESSx; H 5. 4.5 Bandwidth Selection Examples A first example for a bimodal data set generated with equal probability from 1 is presented in Fig. 4. The standard deviation for each distribution (measured before amalgamating the ....
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J. Lin, "Divergence Measures Based on the Shannon Entropy," IEEE Trans. Information Theory, vol. 37, pp. 145-151, 1991.
Enhanced Word Clustering for Hierarchical Text Classification - Dhillon, Mallela, Kumar (4 citations) (Correct)
....word clusters at a high computational cost. In this paper, we rst derive a global criterion that captures the optimality of word clustering in an informationtheoretic framework. This leads to an objective function for clustering that is based on the generalized Jensen Shannon divergence[20] among an arbitrary number of probability distributions. In order to nd the best word clustering, i.e. the clustering that minimizes this objective function, we present a new divisive algorithm for clustering words. This algorithm is reminiscent of the k means algorithm but uses Kullback Leibler ....
....; p2) 1. In contrast, the Jensen Shannon(JS) divergence between p1 and p2 de ned by JS (p1 ; p2) 1KL(p1 ; 1p1 2p2) 2KL(p2 ; 1p1 2p2) H( 1p1 2p2) 1H(p1) 2H(p2) where 1 2 = 1, i 0, is clearly a measure that is symmetric in f 1 ; p1g and f 2 ; p2g, and is bounded [20]. The JS divergence can be generalized to measure the distance between any nite number of probability distributions as: JS (fp i : 1 i ng) H n i p i n i H(p i ) 1) which is symmetric in the f i ; p i g s ( i i = 1; i 0) Let Y be another random variable with ....
J. Lin. Divergence measures based on the Shannon entropy. IEEE Trans. Inform. Theory, 37(1), 1991.
Link Analysis Ranking Algorithms Theory And Experiments - Borodin, Roberts.. (2004) (Correct)
....5 38.56 6157 5 shakespeare 4383 3660 1247 13575 2 3.70 1199 6 table tennis 1948 1489 803 5465 2 3.67 745 6 weather 8011 6464 2852 34672 3 5.36 2775 9 vintage cars 3460 2044 1920 12796 3 6. 26 1580 5 Table 1: Query statistics 31 entries is 1, and then taking the Jensen Shannon divergence [36] of the two distributions. This corresponds to the information we lose about the entries of the vectors if we merge them [50] Any other hierarchical algorithm for clustering binary vectors would also be applicable. Executing the algorithm on the rows of the matrix produces a tree, where each node ....
J. Lin. Divergence measures based on the Shannon entropy. Machine Learning, 37(1): 145--151, 1991.
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....flies would generate the word W . Thus, we measure how well we can discriminate between one individual and a mixture of all the other individuals in the ensemble, or e#ectively how far each individual is from the mean of her conspecifics. The measure I T (W ; identity) has been discussed by Lin [101] as the Jensen Shannon divergence D JS among the distributions P (W ) namely D JS (P (W ) P (W ) P (W ) We recall that the problem of finding a measure of similarity among distributions is not simple; obvious choices such as the Kullback Leibler [35] divergence are not ....
....that the problem of finding a measure of similarity among distributions is not simple; obvious choices such as the Kullback Leibler [35] divergence are not symmetric, and may have spurious technical requirements such as absolute continuity of one distribution with respect to the others. Lin [101] and Guttman [58] proposed D JS as a way of getting around these di#culties, and showed that D JS can be used to bound other measures of similarity, such as the optimal or Bayesian probability of identifying correctly the origin of a sample (as in forced choice psychophysical discrimination ....
J. Lin. Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1):145--151, 1991.
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J. Lin. Divergence measures based on the Shannon entropy. IEEE Trans. Inform. Theory, 37(1): 145--151, 1991.
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J. Lin. Divergence measures based on the Shannon entropy. IEEE Trans. Inform. Theory, 37(1): 145--151, 1991.
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Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Trans. Inform. Theory, 37, 145 --151.
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