نتایج جستجو برای: distance norm

تعداد نتایج: 280572  

2017
Jianxin Wu

2 Distance metrics and similarity measures 2 2.1 Distance metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Vector norm and metric . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 The `p norm and `p metric . . . . . . . . . . . . . . . . . . . . . 3 2.4 Distance metric learning . . . . . . . . . . . . . . . . . . . . . . . 6 2.5 The mean as a similarity measure . . . . . . ...

2014
Hua Wang Feiping Nie Heng Huang

Traditional distance metric learning with side information usually formulates the objectives using the covariance matrices of the data point pairs in the two constraint sets of must-links and cannotlinks. Because the covariance matrix computes the sum of the squared l2-norm distances, it is prone to both outlier samples and outlier features. To develop a robust distance metric learning method, ...

2000
Charu C. Aggarwal Alexander Hinneburg Daniel A. Keim

In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a efficiency and/or effectiveness perspective. Recent research results show that in high dimensional space, ...

2001
Charu C. Aggarwal Alexander Hinneburg Daniel A. Keim

In recent years, the eeect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a eeciency and/or eeectiveness perspective. Recent research results show that in high dimensional space, the ...

2017
Kimmo Eriksson Pontus Strimling Per A. Andersson Mark Aveyard Markus Brauer Vladimir Gritskov Toko Kiyonari David M. Kuhlman Angela T. Maitner Zoi Manesi Catherine Molho Leonard S. Peperkoorn Muhammad Rizwan Adam W. Stivers Qirui Tian Paul A. M. Van Lange Irina Vartanova Junhui Wu Toshio Yamagishi

Violators of cooperation norms may be informally punished by their peers. How such norm enforcement is judged by others can be regarded as a meta-norm (i.e., a second-order norm). We examined whether meta-norms about peer punishment vary across cultures by having students in eight countries judge animations in which an agent who over-harvested a common resource was punished either by a single p...

2014
Alberto Vega Juan Aguarón Jorge García-Alcaraz José María Moreno-Jiménez

TOPSIS is a multicriteria decision making technique based on the minimization of geometric distances that allows the ordering of compared alternatives in accordance with their distances from the ideal and anti-ideal solutions. The technique, that usually measures distances in the Euclidean norm, implicitly supposes that the contemplated attributes are independent. However, as this rarely occurs...

2012
S. Subramanian

A natural way of viewing an inequality or a poverty measure is in terms of the vector distance between an actual (empirical) distribution of incomes and some appropriately normative distribution (reflecting a perfectly equal distribution of incomes, or a distribution with the smallest mean that is compatible with a complete absence of poverty). Real analysis offers a number of distance function...

2012
Nan Shi Yili Hong Kanliang Wang

The referral incentive programs are one of the common incentive mechanisms to attract new customers in e-commerce websites, especially for start-ups, by leveraging existing customer’s social networks. Designing an appropriate referral reward program will allow online businesses to increase customer base and enhance sales. This paper leverages the ultimatum game (sense of fairness) and construal...

Journal: :Computers & Graphics 2002
Sun-Jeong Kim Chang-Hun Kim David Levin

This paper proposes a mesh simplification algorithm using a discrete curvature norm. Most of the simplification algorithms are using a distance metric to date. The distance metric is very efficient to measure geometric error, but it is difficult to distinguish important shape features such as a high-curvature region even though it has a small distance metric. We suggest a discrete curvature nor...

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