Dupont, Stéphane ; Université de Mons > Faculté Polytechnique > Information, Signal et Intelligence artificielle
Ravet, Thierry ; Université de Mons > Faculté Polytechnique > Information, Signal et Intelligence artificielle
Language :
English
Title :
Improved Audio Classification using a Novel Non-Linear Dimensionality Reduction Ensemble Approach
Publication date :
04 November 2013
Event name :
14th International Society for Music Information Retrieval Conference, ISMIR
Event place :
Curitiba, Brazil
Event date :
2013
Research unit :
F105 - Information, Signal et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
Amir Amit. Ensemble Classification via Dimensionality Reduction. Master’s thesis, Efi Arazi School of Computer Science, Israel, 2011.
Leo Breiman. Bagging predictors. Technical Report 421, Department of Statistics, University of California, Berkeley, California, September 1994.
Leo Breiman. Random forests. Mach. Learn., 45(1):5–32, October 2001.
Gavin Brown, Jeremy L. Wyatt, Rachel Harris, and Xin Yao. Diversity creation methods: a survey and categorisation. Information Fusion, 6(1):5–20, 2005.
O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
Stéphane Dupont, Thierry Ravet, Cécile Picard, and Christian Frisson. Nonlinear dimensionality reduction approaches applied to music and textural sounds. In Proceeding of IEEE International Conference on Multimedia and Expo (ICME), San Jose, California, jul 2013.
Xiaoli Zhang Fern and Carla E. Brodley. Random projection for high dimensional data clustering: A cluster ensemble approach. In ICML’03, pages 186–193, 2003.
Geoffrey Hinton and Sam Roweis. Stochastic neighbor embedding. Advances in neural information processing systems, 15:833–840, 2003.
Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell., 20(8):832–844, August 1998.
S. Lafon and A.B. Lee. Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(9):1393 –1403, sept. 2006.
John A. Lee and Michel. Verleysen. Nonlinear dimensionality reduction. Springer, New York; London, 2007.
Christopher J. Merz and Michael J. Pazzani. A principal components approach to combining regression estimates. Mach. Learn., 36(1-2):9–32, July 1999.
Nikunj C. Oza and Kagan Tumer. Dimensionality reduction through classifier ensembles. Technical Report NASA-ARC-IC-1999-124, National Aeronautics and Space Administration, Moffett Field, CA, 1999.
Nikunj C. Oza and Kagan Tumer. Input decimation ensembles: Decorrelation through dimensionality reduction. In LNCS, pages 238–247. Springer, 2001.
Geoffroy Peeters. A large set of audio features for sound description (similarity and classification). in the CUIDADO project. Paris, IR-CAM, 2004.
Juan J. Rodriguez, Ludmila I. Kuncheva, and Carlos J. Alonso. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell., 28(10):1619–1630, October 2006.
Niall Rooney, David Patterson, Alexey Tsymbal, and Sarab An. Random subspacing for regression ensembles. Technical report, Department of Computer Science, Trinity College Dublin, Ireland, 2004.
Robert E. Schapire. The strength of weak learnability. Mach. Learn., 5(2):197–227, July 1990.
Robert E. Schapire and Yoav Freund. Boosting: Foundations and Algorithms. The MIT Press, 2012.
Alon Schclar and Lior Rokach. Random projection ensemble classifiers. In Joaquim Filipe and José Cordeiro, editors, Enterprise Information Systems, 11th International Conference, ICEIS 2009, Milan, Italy, May 6-10, 2009. Proceedings, volume 24 of Lecture Notes in Business Information Processing, pages 309–316. Springer, 2009.
Kagan Tumer and Joydeep Ghosh. Linear and order statistics combiners for pattern classification. CoRR, cs.NE/9905012, 1999.
Laurens van der Maaten and G.E. Hinton. Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research, 9:2579–2605, 2008.
L.J.P. van der Maaten, E. O. Postma, and H. J. van den Herik. Dimensionality reduction: A comparative review. Technical Report TiCC-TR 2009-005, Tilburg University, 2009.
Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, 2012.
