Mahalanobis metrick-meanstime-varying group fixed effects.Primary C13, C23, C38, C63Secondary 62J12In this paper, we propose a Mahalanobis metric based k-means algorithm (KMM) for group membership estimation in linear panel data models with time-varying grouped fixed-effects by Bonhomme and ...
(1980), "Bias Reduction Using Mahalanobis-Metric Matching", Biometrics, 36, 293-298.Rubin D. (1980), `Bias reduction using Mahalanobis-metric matching', Biometrics, 36, pp. 293-298.Rubin, D.B. (1980), 'Bias Reduction Using Mahalanobis Metric Matching', Biomet- rics, 36, 293-298....
In summary, this paper addresses a general problem of learning a Mahalanobis distance metric from side information. It is formulated as a constrained optimization problem, in which the ratio of distances (in terms of ratio of matrix traces) is used as the objective function. An optimization algor...
Maxwell Normal Distribution in a Manifold and Mahalanobis Metric Yukihiko Yamashita, Mariko Numakami, and Naoya Inoue Graduate School of Science and Engineering, Tokyo Institute of Technology, 2-12-1-S6-19, O-okayama, Meguro-ku, Tokyo 152-8553, Japan {yamasita, numakami, n708i}@ide.titech....
st: psmatch2 - exact matching with mahalanobis metric FromVincenzo Mariani <vincemar81@gmail.com> ToStatalist <statalist@hsphsun2.harvard.edu> Subjectst: psmatch2 - exact matching with mahalanobis metric DateTue, 2 Oct 2012 15:17:42 +0200...
The new model is based on the i-vector,and the score between two whitened i-vectors is the Mahalanobis distance. In our paper,the information geometry metric learn algorithm is used to construct the distance metric in the model. The results on NIST 2008 telephone data show that the ...
Constructing a suitable distance metric for scene recognition is a very challenging task due to the huge intra-class variations. In this paper, we propose a novel framework for learning a full parameter matrix in Mahalanobis metric, where the learning process is formulated as a non-negatively cons...
[22].Thus it can enhance the performance of clustering or classification algorithms,such as KNN classifier.Such advantages can be used to perform spe-cial tasks on a given data set, if given a suitable Mahalanobis distance metric. It is natural to learn it from some prior knowledge supplied ...
mahalanobis metric classificationwhitening transformDiabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. ...
This paper deals with the performance study of the clustering algorithm using Euclidean distance metric and Mahalanobis metric. The choice of k-values as the initial estimate of the mean is considered in the second and fifth iterations. The BIRCH-3 and Mopsi-Finland datasets [1] are chosen as...