Learning a Kernel Matrix Using Some Similar and Dissimilar pairs
Fuente:
Redalyc
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Artículo científico |
| Langue: | en |
| Publié: |
International Journal of Combinatorial Optimization Problems and Informatics
2014
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1876484960746995712 |
|---|---|
| author | Hamideh Hajiabadi |
| author_facet | Hamideh Hajiabadi |
| contents | Learning a Kernel Matrix Using Some Similar and Dissimilar pairs Hamideh Hajiabadi Computación Kernel Learning Kernel function Mahanalobis d istance Distance metric learning A lot of machine learning algorithms are based on metric functions, which good functions lead to better results. D istance metric learning has been widely attracted by researchers in last decade. Kernel matrix is somehow a distance function which indicates the similarity between two instances in the feature space which contains high dimensions. Traditional distance metric learning approaches are based on Mahanalobis distance which result in optimiz ing a positive semi definite problem. This kind of approaches need high computational time and do not work well in the case of data with high dimensions. Another filed which is involved by researchers in last decade is building a good kernel matrix which separate non separable data best. This paper proposed a new al gorithm in order to learn kernel matrix which is based on distance metric learning. It is implemented and applied to several standard data sets and the results are shown. 2014 artículo científico 2007-1558 https://www.redalyc.org/articulo.oa?id=265232976003 en http://www.redalyc.org/revista.oa?id=2652 International Journal of Combinatorial Optimization Problems and Informatics application/pdf International Journal of Combinatorial Optimization Problems and Informatics International Journal of Combinatorial Optimization Problems and Informatics (México) Num.1 Vol.5 |
| format | Artículo científico |
| id | redalyc_265232976003 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | International Journal of Combinatorial Optimization Problems and Informatics |
| spellingShingle | Learning a Kernel Matrix Using Some Similar and Dissimilar pairs Hamideh Hajiabadi Computación Kernel Learning Kernel function Mahanalobis d istance Distance metric learning Learning a Kernel Matrix Using Some Similar and Dissimilar pairs Hamideh Hajiabadi Computación Kernel Learning Kernel function Mahanalobis d istance Distance metric learning A lot of machine learning algorithms are based on metric functions, which good functions lead to better results. D istance metric learning has been widely attracted by researchers in last decade. Kernel matrix is somehow a distance function which indicates the similarity between two instances in the feature space which contains high dimensions. Traditional distance metric learning approaches are based on Mahanalobis distance which result in optimiz ing a positive semi definite problem. This kind of approaches need high computational time and do not work well in the case of data with high dimensions. Another filed which is involved by researchers in last decade is building a good kernel matrix which separate non separable data best. This paper proposed a new al gorithm in order to learn kernel matrix which is based on distance metric learning. It is implemented and applied to several standard data sets and the results are shown. 2014 artículo científico 2007-1558 https://www.redalyc.org/articulo.oa?id=265232976003 en http://www.redalyc.org/revista.oa?id=2652 International Journal of Combinatorial Optimization Problems and Informatics application/pdf International Journal of Combinatorial Optimization Problems and Informatics International Journal of Combinatorial Optimization Problems and Informatics (México) Num.1 Vol.5 |
| title | Learning a Kernel Matrix Using Some Similar and Dissimilar pairs |
| topic | Computación Kernel Learning Kernel function Mahanalobis d istance Distance metric learning |
| url | https://www.redalyc.org/articulo.oa?id=265232976003 |