Kullback–Leibler Divergence Metric Learning

Ji, Shuyi; Zhang, Zizhao; Ying, Shihui; Wang, Liejun; Zhao, Xibin; Gao, Yue

doi:10.1109/tcyb.2020.3008248

Cited by 68 publications

(17 citation statements)

References 47 publications

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“…The results will then be compared to the a distribution showing an equal superposition of each eighth note subdivision. This will be done by computing the Kullback-Leibler Divergence (KLD) [16] between the averaged distributions of each of the encoding methods against the equal superposition produced by the quantum circuit in figure 17. Figure 18 shows the results for the spinal cord register for both the simulator and IBMQ Manhattan quantum computer for both the Static and PKBSE encoding methods.…”

Section: Phase Kick Back Results and Analysismentioning

confidence: 99%

QuiKo: A Quantum Beat Generation Application

Oshiro¹

2022

Preprint

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Section: Phase Kick Back Results and Analysismentioning

confidence: 99%

QuiKo: A Quantum Beat Generation Application

Oshiro¹

2022

Preprint

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“…2.4.3. Jensen-Shannon Divergence JS divergence [24] is a symmetric divergence measurement based on Kullback-Leibler divergence [25]. By calculating divergences between histograms, a larger divergence indicates smaller correlative relation and smaller similarity between the histograms.…”

Section: Chi-square Distancementioning

confidence: 99%

Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

2022

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The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases.

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“…This shows that the distance function is very important in social life [8]. Common distance functions include Euclidean distance [9], Manhattan distance [10], Chebyshev distance [11], standardized Euclidean distance [12], Mahalanobis distance [13], Bhattacharyya distance [14], Kullback-Leibler divergence [15], Hamming distance [16] and cosine distance [17]. Next, we introduce these distance functions in detail.…”

Section: Traditional Distance Functionsmentioning

confidence: 99%

Z Distance Function for KNN Classification

Zhang,

2021

Preprint

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This paper proposes a new distance metric function, called Z distance, for KNN classification. The Z distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the Z distance of two data points includes their class center distance and real distance. And its shape looks like "Z". In this way, the affinity of two data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the Z distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification.

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Kullback–Leibler Divergence Metric Learning

Cited by 68 publications

References 47 publications

QuiKo: A Quantum Beat Generation Application

QuiKo: A Quantum Beat Generation Application

Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

Z Distance Function for KNN Classification

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