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A Comparative Experiment on Dimensional Reduction Methods Applicable for Dissimilarity-Based Classifications
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 Title & Authors
A Comparative Experiment on Dimensional Reduction Methods Applicable for Dissimilarity-Based Classifications
Kim, Sang-Woon;
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 Abstract
This paper presents an empirical evaluation on dimensionality reduction strategies by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is the high dimensionality of the dissimilarity space when a lots of objects are treated. To address this issue, two kinds of solutions have been proposed in the literature: prototype selection (PS)-based methods and dimension reduction (DR)-based methods. In this paper, instead of utilizing the PS-based or DR-based methods, a way of performing DBC in Eigen spaces (ES) is considered and empirically compared. In ES-based DBC, classifications are performed as follows: first, a set of principal eigenvectors is extracted from the training data set using a principal component analysis; second, an Eigen space is expanded using a subset of the extracted and selected Eigen vectors; third, after measuring distances among the projected objects in the Eigen space using -norms as the dissimilarity, classification is performed. The experimental results, which are obtained using the nearest neighbor rule with artificial and real-life benchmark data sets, demonstrate that when the dimensionality of the Eigen spaces has been selected appropriately, compared to the PS-based and DR-based methods, the performance of the ES-based DBC can be improved in terms of the classification accuracy.
 Keywords
dissimilarity-based classification(DBC);prototype selection based methods;dimension reduction based methods;Eigen space based methods;
 Language
Korean
 Cited by
 References
1.
S. -W. Kim and R. P. W. Duin, "On optimizing dissimilarity-based classifier using multi-level fusion strategies (in Korean)," Journal of the Institute of Electronics Engineers of Korea, vol. 45, no. CI-5, pp. 15-24, 2008.

2.
R. P. W. Duin, "The dissimilarity representation for finding universals from particulars by an anti-essentialist approach," Pattern Recognition Letters, vol. 64, pp. 37-43, 2015. crossref(new window)

3.
J. Laub, V. Roth, J. M. Buhmann, and K.-R. Muller, "On the information and representation of non-Euclidean pairwise data," Pattern Recognition, vol. 39, pp. 1815-1826, 2006. crossref(new window)

4.
S. -W. Kim and S. -H. Kim, "On optimizing dissimilarity-based classifications using a DTW and fusion strategies (in Korean)," Journal of the Institute of Electronics Engineers of Korea, vol. 47, no. CI-2, pp. 212-219, 2010.

5.
E. Pekalska, R. P. W. Duin, and P. Paclik, "Prototype selection for dissimilarity-based classifiers," Pattern Recognition, vol. 39, pp. 189-208, 2006. crossref(new window)

6.
K. Riesen, V. Kilchherr, and H. Bunke, "Reducing the dimensionality of vector space embeddings of graphs," in Proc. of 5th Int'l Conf. on Machine Learning and Data Mining, vol. LNAI-4571 pp. 563-573, 2007.

7.
S. -W. Kim, "An empirical evaluation on dimensionality reduction schemes for dissimilarity -based classifications," Pattern Recognition Letters, vol. 32, pp. 816-823, 2011. crossref(new window)

8.
S. -W. Kim, "Dissimilarity representations using lp-norms in Eigen spaces," in Proc. of the 2015 Int'l Conf. on Image Processing, Computer Vision, Pattern Recognition, Las Vegas, Nevada, CSREA Press, pp. 242-248, 2015.

9.
D. He and J. Wang, "Parallel computing of eigenvalue of doubly stochastic matrix," in Proc. of the 5th Int'l Conf. on Algorithms and Architecture for Parallel Processing, Beijing, China, pp. 355-358, 2002.

10.
R. P. W. Duin, M. Bicego, M. Orozco-Alzate, S. -W. Kim, and M. Loog, "Metric learning in dissimilarity space for improved nearest neighbor performance," in Proc. of S+SSPR2014, Joensuu, Finland, LNCS8621, pp. 183-192, 2014.

11.
N. Kwak, "Principal component analysis based on l1-norm maximization," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1672-1680, 2008. crossref(new window)