TY - GEN
T1 - Efficient semantic kernel-based text classification using matching pursuit KFDA
AU - Zhang, Qing
AU - Li, Jianwu
AU - Zhang, Zhiping
PY - 2011
Y1 - 2011
N2 - A number of powerful kernel-based learning machines, such as support vector machines (SVMs), kernel Fisher discriminant analysis (KFDA), have been proposed with competitive performance. However, directly applying existing attractive kernel approaches to text classification (TC) task will suffer semantic related information deficiency and incur huge computation costs hindering their practical use in numerous large scale and real-time applications with fast testing requirement. To tackle this problem, this paper proposes a novel semantic kernel-based framework for efficient TC which offers a sparse representation of the final optimal prediction function while preserving the semantic related information in kernel approximate subspace. Experiments on 20-Newsgroup dataset demonstrate the proposed method compared with SVM and KNN (K-nearest neighbor) can significantly reduce the computation costs in predicating phase while maintaining considerable classification accuracy.
AB - A number of powerful kernel-based learning machines, such as support vector machines (SVMs), kernel Fisher discriminant analysis (KFDA), have been proposed with competitive performance. However, directly applying existing attractive kernel approaches to text classification (TC) task will suffer semantic related information deficiency and incur huge computation costs hindering their practical use in numerous large scale and real-time applications with fast testing requirement. To tackle this problem, this paper proposes a novel semantic kernel-based framework for efficient TC which offers a sparse representation of the final optimal prediction function while preserving the semantic related information in kernel approximate subspace. Experiments on 20-Newsgroup dataset demonstrate the proposed method compared with SVM and KNN (K-nearest neighbor) can significantly reduce the computation costs in predicating phase while maintaining considerable classification accuracy.
KW - Efficient Text Classification
KW - Kernel Method
KW - Matching Pursuit KFDA
KW - Semantic Kernel
UR - http://www.scopus.com/inward/record.url?scp=81855177430&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24958-7_45
DO - 10.1007/978-3-642-24958-7_45
M3 - Conference contribution
AN - SCOPUS:81855177430
SN - 9783642249570
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 382
EP - 390
BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
T2 - 18th International Conference on Neural Information Processing, ICONIP 2011
Y2 - 13 November 2011 through 17 November 2011
ER -