马氏距离(Mahalanobis Distence) 是度量学习(metric learning)中一种常用的测度,所谓测度/距离函数/度量(metric)也就是定义一个空间中元素间距离的函数,所谓度量学习也叫做相似度学习。 什么是马氏距离 似乎是一种更好度量相似度的方法。 马氏距离是基于样本分布的一种距离。 物理意义就是在规范化的主成分空间中的欧...
Summary: Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful ...
The "Mahalanobis distance" is ametric(a rule for calculating the distance between two points) which is better adapted than the usual "Euclidian distance" to settings involving non spherically symmetric distributions. It is more particularly useful whenmultinormaldistributions are involved, although its ...
Still, the Mahalanobis metric remains a heuristic for partitioning, although generally a better one than the Euclidean metric. When the surface is too complex to be neatly partitioned into two clearly disjoint surfaces, the use of the Mahalanobis distance metric can produce an imbalanced partitioning...
An Improved algorithm for image recognition, called Mahalanobis Distance Metric based Laplacian Mapping Algorithm(MLMA), is presented in this paper. Firstly MLMA learns a Mahalanobis metric matrix from training samples, then we use the Mahalanobis metic as a similarity measure in Laplacian Mapping Algor...
Distancemetricisakeyissueinmanymachinelearningalgo-rithms.Forexample,KmeansandK-nearestneighbor(KNN)classifierneedtobesuppliedasuitabledistancemetric,throughwhichneigh-boringdatapointscanbeidentified.ThecommonlyusedEuclideandistancemetricassumesthateachfeatureofdatapointisequallyimportantandindependentfromothers.Thisassumptio...
编辑距离:Levenshtein Distance算法 题目链接:https://cn.vjudge.net/problem/51Nod-1183 Levenshtein距离是一种计算两个字符串间的差异程度的字符串度量(string metric)。我们可以认为Levenshtein距离就是从一个字符串修改到另一个字符串时,其中编辑单个字符(比如修改、插入、删除)所需要的最少次数。俄罗斯科学家Vladimi...
Learning a Mahalanobis distance metric for data clustering and classification Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the for... S Xiang,F Nie,C Zhang - 《Pattern Recognition》 被引量: 62...
编辑距离:Levenshtein Distance算法 题目链接:https://cn.vjudge.net/problem/51Nod-1183 Levenshtein距离是一种计算两个字符串间的差异程度的字符串度量(string metric)。我们可以认为Levenshtein距离就是从一个字符串修改到另一个字符串时,其中编辑单个字符(比如修改、插入、删除)所需要的最少次数。俄罗斯科学家Vladimi...
However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e....