|
[1]. Roweis, Sam T. and Saul, Lawrence K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. SCIENCE. 2000, Vol. 290. [2]. Maaten, L.J.P. van der. An Introduction to Dimensionality Reduction Using Matlab. Maastricht University, Maastricht, The Netherlands : Technical Report MICC 07-07, 2007. [3]. Fodor, Imola K. A survey of dimension reduction techniques. s.l. : LLNL technical report, 2002. [4]. Visualization using Locally Linear Embedding method. Nhut, Nguyen Minh. 2006. [5]. Kruskal, Joseph B. and Wish, Myron. Multidimensional scaling. s.l. : Sage Publications, Inc, 1978. 0803909403. [6]. Tenenbaum, Joshua B., Silva, Vin de and Langford, John C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. SCIENCE. 2000, Vol. 290. [7]. Saul, Lawrence K. and Roweis, Sam T. An Introduction to Locally Linear Embedding. [8]. Parallelize Data Dimensionality Reduction Techniques on Many-Core Graphic Processing Hardware. Yeh, Tsung-Tai. 2009. [9]. Nearest neighbor queries. Roussopoulos, Nick, Kelley, Stephen and Vincent, Frédéric. San Jose, California, United States : Proceedings of the 1995 ACM SIGMOD international conference on Management of data, 1995. 0-89791-731-6. [10]. Hern´andez, V., et al. Arnoldi Methods in SLEPc. s.l. : SLEPc Technical Report, 2006. [11]. Donoho, David L. and Grimes, Carrie. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. s.l. : Proceedings of the National Academy of Arts and Sciences. 5591–5596. [12]. Pan, Yaozhang, Ge, Shuzhi Sam and Mamun, Abdullah Al. Weighted locally linear embedding for dimension reduction. Pattern Recognition. June 2004, Vol. 42. [13]. Zhou, Kun, et al. Real-time KD-tree construction on graphics hardware. ACM Transactions on Graphics . December 2008, Vol. 27. [14]. Fast k nearest neighbor search using GPU. Garcia, V., Debreuve, E. and Barlaud, M. s.l. : Proceedings of the CVPR Workshop on Computer Vision on GPU, June 2008. [15]. Interactive k-D Tree GPU Raytracing. R., HORN D., et al. s.l. : Proceedings of the 2007 symposium on Interactive 3D graphicsand games, ACM, 2007. [16]. Bell, N. and Garland, M. Efficient Sparse Matrix-Vector Multiplication on CUDA. s.l. : NVIDIA Technical Report NVR-2008-004, NVIDIA Corporation, December 2008. [17]. Baskaran, Muthu Manikandan and Bordawekar, Rajesh. Optimizing Sparse Matrix-Vector Multiplication on GPUs. s.l. : IBM Research Report, 2008. [18]. Corporation, NVIDIA. Cublas Library. s.l. : NVIDIA Corporation, June 2009. [19]. MLLE: Modified Locally Linear Embedding Using MultipleWeights. Wang, Jing and Zhang, Zhenyue. Cambridge : Advances in Neural Information Processing Systems 19, 2007. [20]. Seidl, Thomas and Kriegel, Hans-Peter. Optimal multi-step k-nearest neighbor search. ACM SIGMOD Record. June 1998, Vol. 27. [21]. Efficient reverse k-nearest neighbor search in arbitrary metric spaces. Achtert, Elke, et al. Chicago, IL, USA : Proceedings of the 2006 ACM SIGMOD international conference on Management of data, 2006. [22]. Orenstein, J. A. Multidimensional Tries Used for Associative Searching. Information Processing Letters. June 1982, Vols. 14, No. 4, pp. 150-157. [23]. Highly parallel fast KD-tree construction for Interactive ray tracing of dynamic scenes. M., SHEVTSOV, A., SOUPIKOV and A., KAPUSTIN. s.l. : Computer Graphics Forum 26, September, 2007. [24]. Hern´andez, V., et al. Lanczos Methods in SLEPc. s.l. : SLEPc Technical Report, 2006. [25]. Saad, Y. Krylov subspace methods for solving large unsymmetric linear systems. Mathematics of computation. July, 1981, Vol. 37. [26]. Hernandez, V., Roman, J. E. and Tomas, A. Parallel Arnoldi eigensolvers with enhanced scalability via global communications rearrangement. Parallel Computing. August 2007, Vols. 33, Issue 7-8, Pages: 521-540.
|