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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 29 / No.1: The Twenty-Ninth Conference on Artificial Intelligence

SP-SVM: Large Margin Classifier for Data on Multiple Manifolds

March 8, 2023

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Authors

Bin Shen

Purdue University


Bao-Di Liu

China University of Petroleum, Qingdao


Qifan Wang

Purdue University


Yi Fang

Santa Clara University


Jan Allebach

Purdue University


DOI:

10.1609/aaai.v29i1.9611


Abstract:

As one of the most important state-of-the-art classification techniques, Support Vector Machine (SVM) has been widely adopted in many real-world applications, such as object detection, face recognition, text categorization, etc., due to its competitive practical performance and elegant theoretical interpretation. However, it treats all samples independently, and ignores the fact that, in many real situations especially when data are in high dimensional space, samples typically lie on low dimensional manifolds of the feature space and thus a sample can be related to its neighbors by being represented as a linear combination of other samples on the same manifold. This linear representation, which is usually sparse, reflects the structure of underlying manifolds. It has been extensively explored in the recent literature and proven to be critical for the performance of classification. To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP-SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes. Consequently, SP-SVM inherits both the discriminative power of support vector machine and the merits of sparsity. A set of experiments on real-world benchmark data sets show that SP-SVM achieves significantly higher precision on recognition task than various competitive baselines including the traditional SVM, the sparse representation based method and the classical nearest neighbor classifier.

Topics: AAAI

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HOW TO CITE:

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach SP-SVM: Large Margin Classifier for Data on Multiple Manifolds Proceedings of the AAAI Conference on Artificial Intelligence, 29 (2015) .

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach SP-SVM: Large Margin Classifier for Data on Multiple Manifolds AAAI 2015, .

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach (2015). SP-SVM: Large Margin Classifier for Data on Multiple Manifolds. Proceedings of the AAAI Conference on Artificial Intelligence, 29, .

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach. SP-SVM: Large Margin Classifier for Data on Multiple Manifolds. Proceedings of the AAAI Conference on Artificial Intelligence, 29 2015 p..

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach. 2015. SP-SVM: Large Margin Classifier for Data on Multiple Manifolds. "Proceedings of the AAAI Conference on Artificial Intelligence, 29". .

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach. (2015) "SP-SVM: Large Margin Classifier for Data on Multiple Manifolds", Proceedings of the AAAI Conference on Artificial Intelligence, 29, p.

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach, "SP-SVM: Large Margin Classifier for Data on Multiple Manifolds", AAAI, p., 2015.

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach. "SP-SVM: Large Margin Classifier for Data on Multiple Manifolds". Proceedings of the AAAI Conference on Artificial Intelligence, 29, 2015, p..

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach. "SP-SVM: Large Margin Classifier for Data on Multiple Manifolds". Proceedings of the AAAI Conference on Artificial Intelligence, 29, (2015): .

Bin Shen|| Bao-Di Liu|| Qifan Wang|| Yi Fang|| Jan Allebach. SP-SVM: Large Margin Classifier for Data on Multiple Manifolds. AAAI[Internet]. 2015[cited 2023]; .


ISSN: 2374-3468


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