Efficient Kernel Discriminative Geometry Preserving Projection for Document Classification

• Autorzy: Wang Z. , Sun X. , Qian X.

Warianty tytułu

PL

Algorytm KDGPP do identyfikacji I klasyfikacji dokumentów

• Języki publikacji: EN

Abstrakty

EN

A new dimensionality reduction algorithm called kernel discriminative geometry preserving projection (KDGPP) is proposed to cope with document classification. By considering both intraclass geometry and interclass discrimination, KDGPP can not only nonlinearly project documents into lower-dimensional feature space via manifold adaptive kernel function but also reduce the computational complexity with Nyström method. Experimental results demonstrate that KDGPP outperforms other related algorithms in terms of effectiveness and efficiency.

PL

Zaproponowano nowy algorytm do klasyfikacji dokumentów nazwany KDGPP – kernel discriminative geometry preserving projection. Algorytm redukuje złożoność obliczeń numerycznych.

Słowa kluczowe

EN

document classification; KDGPP

PL

klasyfikacja dokumentów; rozpoznawanie znaków

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 5b
• Strony: 56--59
• Opis fizyczny: Bibliogr. 9 poz., tab.

Twórcy

autor

Wang Z.

autor

Sun X.

autor

Qian X.

• Henan University of Technology (1), China University of Mining & Technology(Beijing)

Bibliografia

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• [2] Xu W.,Liu X.,Gong Y.,Document Clustering Based on Nonnegative Matrix Factorization, Proceedings of SIGIR'03, 2003:267-273
• [3] Duda R.O.,Hart P.E.,Stork D.G., Pattern Classification(second edition),Wiley-Interscience, Hoboken, N.J., 2000
• [4] Cai D.,He X.,Han J.,Document Clustering Using Locality Preserving Indexing,IEEE Transactions on Knowledge and Data Engineering, 17(2005), No.12,1624-1637
• [5] Yan S.,Xu D.,Zhang B.,Zhang H.-J.,Yang Q.,Lin S.,Graph Embedding and Extensions: A General Framework for Dimensionality Reduction,IEEE Transactions on Pattern Analysis and Machine Intelligence,29(2007),No.1,40-51
• [6] Song D.,Tao D.,Biologically Inspired Feature Manifold for Scene Classification,IEEE Transactions on Image Processing, 19(2010), No.1,174-184
• [7] Zhuang J., Tsang I.W., Hoi S.C.H.,SimpleNPKL: Simple Non- Parametric Kernel Learning, Proceedings of ICML'09,2009:1273-1280
• [8] Zhang K., Kwok J.T., Clustered Nyström Method for Large Scale Manifold Learning and Dimension Reduction,IEEE Transactions on Neural Networks, 21(2010),No.10,1576-1587
• [9] Yang,Y., An Evaluation of Statistical Approaches to Text Categorization,Jounal of Information Retrieval, 1(1999),No.1-2,67-88