
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Khanh Vu, Kien A. Hua, Hao Cheng, SheauDong Lang, "Bounded Approximation: A New Criterion for Dimensionality Reduction Approximation in Similarity Search," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 6, pp. 768783, June, 2008.  
BibTex  x  
@article{ 10.1109/TKDE.2008.30, author = {Khanh Vu and Kien A. Hua and Hao Cheng and SheauDong Lang}, title = {Bounded Approximation: A New Criterion for Dimensionality Reduction Approximation in Similarity Search}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {20}, number = {6}, issn = {10414347}, year = {2008}, pages = {768783}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.30}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Bounded Approximation: A New Criterion for Dimensionality Reduction Approximation in Similarity Search IS  6 SN  10414347 SP768 EP783 EPD  768783 A1  Khanh Vu, A1  Kien A. Hua, A1  Hao Cheng, A1  SheauDong Lang, PY  2008 KW  Information Storage and Retrieval KW  Information Search and Retrieval KW  Search process VL  20 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] http://sipi.usc.edu/services/databasedatabase.html , 2007.
[2] http://uforia.org/mariohspatialindex/, 2007.
[3] http://www.cs.cmu.edu/ christossoftware.html , 2007.
[4] http://www.cse.ohiostate.edu/õzturk/ datadata, 2007.
[5] http://www.ctisus.org/tfindextf.html, 2007.
[6] http://www.mediateam.oulu.fi/mtdbdownload.html , 2007.
[7] N. Beckman, H. Kriegel, R. Schneider, and B. Seeger, “The ${\rm R}^{\ast}\hbox{}{\rm tree}$ : An Efficient and Robust Access Method for Points and Rectangles,” Proc. ACM SIGMOD '90, pp. 322331, May 1990.
[8] S. Berchtold, C. Bohm, and H. Kriegel, “The Pyramid Technique: Toward Breaking the Curse of Dimensionality,” Proc. ACM SIGMOD '98, pp. 142153, 1998.
[9] S. Berchtold, D. Keim, and H. Kriegel, “The XTree: An Index Structure for HighDimensional Data,” Proc. 22nd Int'l Conf. Very Large Data Bases (VLDB), 1996.
[10] C. Blake and C. Merz, UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/mlearnMLRepository.html , 1998.
[11] J. Bourgain, “On Lipschitz Embedding of Finite Metric Spaces into Hilbert Space,” Israel J. Math., no. 52, pp. 4652, 1985.
[12] J.C. Traina, A. Traina, C. Faloutsos, and B. Seeger, “Fast Indexing and Visualization of Metric Data Sets Using SlimTrees,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 2, pp. 244260, Mar./Apr. 2002.
[13] G. Cha, X. Zhu, D. Petkovic, and C. Chung, “An Efficient Indexing Method for Nearest Neighbor Searches in HighDimensional Image Databases,” IEEE Trans. Multimedia, vol. 4, no. 1, pp. 7687, Mar. 2002.
[14] K. Chan and W. Fu, “Efficient Time Series Matching by Wavelets,” Proc. 15th IEEE Int'l Conf. Data Eng. (ICDE), 1999.
[15] D.L. Donoho, “HighDimensional Data Analysis: The Curses and Blessings of Dimensionality,” Proc. AMS Conf. Math. Challenges of the 21st Century, http://www.waveletidr.orglectures.html, 2000.
[16] O. Egecioglu, H. Ferhatosmanoglu, and U. Ogra, “Dimensionality Reduction and Similarity Computation by InnerProduct Approximations,” IEEE Trans. Knowledge and Eng., vol. 16, no. 6, pp. 714726, June 2004.
[17] C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, “Fast Subsequence Matching in TimeSeries Databases,” Proc. ACM SIGMOD '94, pp. 419429, May 1994.
[18] R.F.S. Filho, A.J.M. Traina, C. T. Jr., and C. Faloutsos, “Similarity Search without Tears: The OMNI Family of AllPurpose Access Methods,” Proc. 17th IEEE Int'l Conf. Data Eng. (ICDE '01), pp. 623630, 2001.
[19] V. Gaede and O. Günther, “Multidimensional Access Methods,” ACM Computing Surveys, vol. 30, no. 2, pp. 170231, 1998.
[20] D. Goldin and P. Kanellakis, “On Similarity Queries for TimeSeries Data: Constraint Specifications and Implementation,” Proc. First Int'l Conf. Principles and Practice of Constraint Programming (CP '95), pp. 137153, Sept. 1995.
[21] A. Guttman, “The RTree: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMOD '84, pp. 4757, June 1984.
[22] K.A. Hua, K. Vu, and J. Oh, “SamMatch: A Flexible and Efficient SamplingBased Image Retrieval Technique for Large Image Databases,” Proc. Seventh ACM Int'l Conf. Multimedia (Multimedia '99), pp. 225234, Oct. 1999.
[23] H.V. Jagadish, B.C. Ooi, K. Tan, C. Yu, and R. Zhang, “iDistance: An Adaptive ${\rm B}^{+}\hbox{}{\rm tree}$ Based Indexing Method for Nearest Neighbor Search,” ACM Trans. Data Base Systems, vol. 30, no. 2, pp. 364397, 2005.
[24] K.V.R. Kanth, D. Agrawal, A.E. Abbadi, and A. Singh, “Dimensionality Reduction for Similarity Searching in Dynamic Databases,” Proc. ACM SIGMOD '98, pp. 166176, 1998.
[25] E. Keogh, K. Chakrabarti, M. Pazzani, and Mehrotra, “Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases,” J. Knowledge and Information Systems, 2000.
[26] E.J. Keogh, K. Chakrabarti, S. Mehrotra, and M.J. Pazzani, “Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases,” Proc. ACM SIGMOD '01, pp. 151162, 2001.
[27] C. Li, P. Yu, and V. Castelli, “Hierarchyscan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences,” Proc. 12th IEEE Int'l Conf. Data Eng. (ICDE '96), pp. 546553, 1996.
[28] A. Natsev, R. Rastogi, and K. Shim, “Walrus: A Similarity Retrieval Algorithm for Image Databases,” Proc. ACM SIGMOD '99, pp. 395406, 1999.
[29] R. Orlandic, J. Lukaszuk, and C. Swietlik, “The Design of a Retrieval Technique for HighDimensional Data on Tertiary Storage,” ACM SIGMOD Record, vol. 31, no. 2, pp. 1521, June 2002.
[30] A. Paradopoulos and Y. Manolopoulos, “Performance of Nearest Neighbor Queries in RTrees,” Proc. Sixth Int'l Conf. Database Theory (ICDT '97), pp. 394408, 1997.
[31] R. Agrawal, C. Faloutsos, and A. Swami, “Efficient Similarity Search in Sequence Databases,” Proc. Fourth Int'l Conf. Foundations of Data Organizations and Algorithms (FODO), 1993.
[32] S. Roweis, “EM Algorithms for PCA and SPCA,” Advances in Neural Information Processing Systems 10, pp. 626632, 1997.
[33] H. Samet, “Foundations of Multidimensional and Metric Data Structures,” The Morgan Kaufmann Series in Computer Graphics, first ed. Morgan Kaufmann, 2006.
[34] T. Seidl and H. Kriegel, “Optimal MultiStep $k\hbox{}{\rm Nearest}\;{\rm Neighbor}$ Search,” Proc. ACM SIGMOD '98, pp. 154165, 1998.
[35] L. Sirovich and R. Everson, “Management and Analysis of Large Scientific Datasets,” Int'l J. Supercomputer Applications, vol. 6, no. 1, pp. 5068, 1992.
[36] K. Vu, K.A. Hua, H. Cheng, and S.D. Lang, “A NonLinear DimensionalityReduction Technique for Fast Similarity Search in Large Databases,” Proc. ACM SIGMOD '06, pp. 527538, 2006.
[37] R. Weber, H. Schek, and S. Blott, “A Quantitative Analysis and Performance Study for SimilaritySearch Methods in HighDimensional Spaces,” Proc. 24th Int'l Conf. Very Large Data Bases (VLDB '98), pp. 194205, 1998.
[38] Y. Wu, D. Agrawal, and A. Abbadi, “A Comparison of DFT and DWT Based Similarity Search in Time Series Databases,” Proc. Ninth ACM Int'l Conf. Information and Knowledge Management (CIKM), 2000.
[39] J. Ye, R. Janardan, and Q. Li, “GPCA: An Efficient Dimension Reduction Scheme for Image Compression and Retrieval,” Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '04), pp. 354363, 2004.
[40] B.K. Yi and C. Faloutsos, “Fast Time Sequence Indexing for Arbitrary ${\rm l}_{p}$ Norms,” The VLDB J., pp. 385394, 2000.
[41] T. Zhang, R. Ramakrishnan, and M. Livny, “Birch: An Efficient Data Clustering Method for Very Large Databases,” Proc. ACM SIGMOD '96, pp. 103114, June 1996.