Optimal complexity for discriminant analysis

• Autorzy: Hosseinzadeh A. , Noorhosseini M.

Warianty tytułu

PL

Analiza optymalnej złożoności w analizie dyskryminacyjnej

• Języki publikacji: EN

Abstrakty

EN

A mechanism has been proposed to achieve optimal complexity for Discriminant Analysis (DA) based on Principal Component Analysis (PCA). We use PCA to filter non-informative features before applying to DA algorithm. In addition to significant accuracy improvement, the mechanism decreases the computational and storage costs of Linear Discriminant Analysis methods and makes the overall method more efficient. The mechanism helps classical linear DA methods outperform the state of the art and most superior linear and non-linear DA methods.

PL

Zaproponowano mechanizm analizy złożoności w analizie dyskryminacyjnej bazującej na Analizie Składowej Głównej PCA. Dodatkowo mechanizm umożliwia poprawę dokładności klasyfikacji danych.

Słowa kluczowe

EN

face recognition; object recognition; discriminant analysis; computer vision; pattern recognition; electrical technology

PL

analiza dyskryminacyjna; PCA; rozpoznawanie obrazu; elektrotechnika

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2011
• Tom: R. 87, nr 7
• Strony: 193--198
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Hosseinzadeh A.

autor

Noorhosseini M.

• Amirkabir University of Technology, Electrical Engineering Dept., Tehran, Iran,

Bibliografia

• [1] Zhu, M., and Martinez, A. M.: "Subclass discriminant analysis, IEEE Trans, on Pattern Analysis and Machine Intelligence", 2006, 28, (8), pp. 1274-1286.
• [2] Loog, M., and Duin, R.P.W.: 'Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion', IEEE Trans, on Pattern Analysis and Machine Intelligence, 2004, 26, (6), pp. 732-739.
• [3] Loog, M., Duin, R.P.W., and Haeb-Umbach, T.: 'Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria', IEEE Trans, on Pattern Analysis and Machine Intelligence, 2001, 23, (7), pp. 762-766.
• [4] Jain, A. K., Duin, R. P.W., and Mao, J.: "Statistical Pattern Recognition: A Review', IEEE Trans, on Pattern Analysis and Machine Intelligence, 2000, 22, (1), pp. 4-37.
• [5] Chandrasekaran, B., and Jain, A. K.: 'Dimensionality and Sample Size Considerations in Pattern Recognition Practice', Krishnaiah, P.R., and Kanał, L.N., (Eds.): 'Handbook of Statistics, Vol. 2' (Amsterdam: North-Holland, 1982), pp. 835-855
• [6] Fukunaga, K.: "Introduction to Statistical Pattern Recognition" (Academic Press, 1990, 2nd edn.).
• [7] Yang, J., Frangi, A. F., Yang, J., Zhang, D., and Jin, Z. : 'KPCA plus LDA: A complete kernel fisher discriminant framework for feature extraction and recognition", IEEE Trans, on Pattern Analysis and Machinę Intelligence, 2005, 27, (2), pp. 230-244.
• [8] Fukunaga, K., and Short, R. D.: 'A class of feature extraction criteria and its relation to the Bayes risk estimate', IEEE Trans. on Inform. Theory, 1978, IT-26, pp. 59-65.
• [9] Alpaydin, E.: 'Introduction to Machine Learning' (MIT Press, 2004).
• [10] Duda, R., Hart, P., and Stork, D.: 'Pattern Classification' (John Wiley&Sons, 2001).
• [11] Hughes, G. F.: 'On the mean accuracy of Statistical pattern recognizers', IEEE Trans. Inform. Theory, 1968, 14, pp. 55-63.
• [12] Georghiades, A.S., Belhumeur, P.N., and Kriegman, D.J.: 'From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose', IEEE Trans, on Pattern Analysis and Machinę Intelligence, 2001, 23, (6), pp. 643-660.
• [13] Lee, K.C., Ho, J., and Kriegman, D.: 'Acquiring Linear Subspaces for Face Recognition under Variable Lighting', IEEE Trans, on Pattern Analysis and Machinę Intelligence, 2005, 27, (5), pp. 684-698.
• [14] Phillips, P.J.: The Facial Recognition Technology (FERET; Database', http://www.itl.nist.gov/iad/humanid/feret/ferel master.html, 2004.
• [15] Phillips, P.J., Moon, H., Rizvi, SA, and Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms', IEEE Trans, on Pattern Analysis and Machine Intelligence, 2000, 22, (10), p. 1090-1104.
• [16] Leibe, B., and Schiele, B.: 'Analyzing Appearance and Gontom Based Methods for Object Categorization', In Proc. IEEEConf. Computer Vision and Pattern Recognition (CVPR), 2003.
• [17] http://www.cl.cam.ac.Uk/research/dtg/attarchive/facedatabase.h tml, accessed August 2010.
• [18] http://www.shef.ac.uk/eee/research/vie/research/face.html, accessed August 2010.
• [19] http://www.anefian.com/research/face_reco.htm, accessei August 2010.
• [20] http://cswww.essex.ac.uk/mv/allfaces/faces94.html, August 2010.
• [21] http://cswww.essex.ac.uk/mv/allfaces/faces95.html, accessei August 2010.