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  4. Making metric learning algorithms invariant to transformations using a projection metric on Grassmann manifolds
 
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Making metric learning algorithms invariant to transformations using a projection metric on Grassmann manifolds

Publikationstyp
Journal Article
Date Issued
2019-12-01
Sprache
English
Author(s)
Goudarzi, Zahra  
Adibi, Peyman  
Grigat, Rolf-Rainer  
Ehsani, Mohammad Saeid  
Institut
Bildverarbeitungssysteme E-2  
TORE-URI
http://hdl.handle.net/11420/3868
Journal
International journal of machine learning and cybernetics  
Volume
10
Issue
12
Start Page
3407
End Page
3416
Citation
International Journal of Machine Learning and Cybernetics 12 (10): 3407-3416 (2019-12-01)
Publisher DOI
10.1007/s13042-019-00927-4
Scopus ID
2-s2.0-85074981753
The requirement for suitable ways to measure the distance or similarity between data is omnipresent in machine learning, pattern recognition and data mining, but extracting such good metrics for particular problems is in general challenging. This has led to the emergence of metric learning ideas, which intend to automatically learn a distance function tuned to a specific task. In many tasks and data types, there are natural transformations to which the classification result should be invariant or insensitive. This demand and its implications are essential in many machine learning applications, and insensitivity to image transformations was in the first place achieved by using invariant feature vectors. In this paper, a new representation model on Grassmann manifolds for data points and a novel method for learning a Mahalanobis metric which uses the geodesic distance on Grassmann manifolds are proposed. In fact, we use an appropriate geodesic distance metric on the Grassmann manifolds, called projection metric, for measuring primary similarities between the new representations of the data points. This makes learning of the Mahalanobis metric invariant to similarity transforms and intensity changes, and therefore improve the performance. Experiments on face and handwritten digit datasets demonstrate that our proposed method yields performance improvements in a state-of-the-art metric learning algorithm.
Subjects
Grassmann manifold
Mahalanobis metric
Metric learning
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