Abstract | ||
---|---|---|
The Support Vector Machine (SVM) has achieved promising classification performance. However, since it is based only on local
information (Support Vectors), it is sensitive to directions with large data spread. On the other hand, Nonparametric Discriminant
Analysis (NDA) is an improvement over the more general Linear Discriminant Analysis (LDA) where, the normality assumption
from LDA is relaxed. Furthermore, NDA incorporates the partially global information to detect the dominant normal directions
to the decision surface, which represent the true data spread. However, NDA relies on the choice of the κ-nearest neighbors
(κ-NN’s) on the decision boundary. This paper introduces a novel Combined SVM and NDA (CSVMNDA) model which controls the spread
of the data, while maximizing a relative margin separating the data classes. This model is considered as an improvement to
SVM by incorporating the data spread information represented by the dominant normal directions to the decision boundary. This
can also be viewed as an extension to the NDA where the support vectors improve the choice of κ-nearest neighbors (κ-NN’s) on the decision boundary by incorporating local information. Since our model is an extension to both SVM and NDA, it can
deal with heteroscedastic and non-normal data. It also avoids the small sample size problem. Interestingly, the proposed improvements
only require a rigorous and simple combination of NDA and SVM objective functions, and preserve the computational efficiency
of SVM. Through the optimization of the CSVMNDA objective function, surprising performance gains were achieved on real-world
problems. In particular, the experiments on face recognition have clearly shown the superiority of CSVMNDA over other state-of-the-art
classification methods, especially, SVM and NDA. |
Year | DOI | Venue |
---|---|---|
2012 | 10.1007/s13042-011-0045-9 | Int. J. Machine Learning & Cybernetics |
Keywords | Field | DocType |
nonparametric discriminant analysis � support vector machinespartially global information � local informationsmall sample size problem � face recognition | Normality,Facial recognition system,Heteroscedasticity,Pattern recognition,Support vector machine,Artificial intelligence,Linear discriminant analysis,Decision boundary,Sample size determination,Mathematics,Machine learning,Nonparametric discriminant analysis | Journal |
Volume | Issue | ISSN |
3 | 2 | 1868-808X |
Citations | PageRank | References |
4 | 0.44 | 23 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Riadh Ksantini | 1 | 82 | 15.39 |
Boubakeur Boufama | 2 | 162 | 22.02 |