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https://research.google/pubs/pub36261/
PSVM: Parallel Support Vector Machines with Incomplete Cholesky Factorization. Edward Y. Chang; Hongjie Bai; Kaihua Zhu; Hao Wang; Jian Li; Zhihuan Qiu; Scaling Up Machine Learning, Cambridge University Press (2010) Google Scholar Copy Bibtex Abstract.Cited by: 7
https://en.wikipedia.org/wiki/Incomplete_Cholesky_factorization
An incomplete Cholesky factorization is often used as a preconditioner for algorithms like the conjugate gradient method. The Cholesky factorization of a positive definite matrix A is A = LL* where L is a lower triangular matrix. An incomplete Cholesky factorization is given by a sparse lower triangular matrix K that is in some sense close to L.
http://www.seas.ucla.edu/~vandenbe/publications/svmcmpl.pdf
Support vector machine training using matrix completion techniques ... Keywords: support vector machines, classification, kernel methods, convex optimization, interior-point methods ... discuss an incomplete Cholesky factorization algorithm for computing a low-rank approximation
https://research.google.com/pubs/archive/34638.pdf
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which
https://link.springer.com/article/10.1023/A:1022323931043
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner for solving dense symmetric positive definite linear systems. This method is suitable for situations where matrices cannot be explicitly stored but each column can be easily computed. Analysis and implementation of this preconditioner are discussed.Cited by: 17
http://wseas.us/e-library/conferences/2013/Dubrovnik/MATHMECH/MATHMECH-25.pdf
Cholesky factorization of sparse matrices. Both sequential and parallel algorithms are explored. Key ingredients of a symbolic factorization as a key step in efficient Cholesky factorization of sparse matrices are also presented. The paper is based on the author’s master thesis [1], defended at Sarajevo School of Science and Technology in 2012.
https://software.intel.com/en-us/forums/intel-math-kernel-library/topic/282181
incomplete Cholesky preconditioner is unsymmetrical so you can't use it with CG Thats's not true. Incomplete Cholesky factorization is given by A = L * L^T, so it is symmetrical by design, in distinction from incomplete LU factorization. Yes, I cannot use LU …
http://students.cs.tamu.edu/jdass/Documents/ICDCS17_1792a753.pdf
Distributed QR decomposition framework for training Support Vector Machines Jyotikrishna Dass, V.N.S. Prithvi Sakuru, Vivek Sarin and Rabi N. Mahapatra Department of Computer Science & Engineering Texas A&M University, College Station, Texas 77840 {dass.jyotikrishna, prithvi.sakuru, sarin, …
https://dl.acm.org/citation.cfm?id=3043600
We're upgrading the ACM DL, and would like your input. Please sign up to review new features, functionality and page designs.Cited by: 3
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/svm.html
As mentioned previously, H2O’s implementation of support vector machine follows the PSVM algorithm specified by Edward Y. Chang and others. This implementation can be used to solve binary classification problems. In this configuration, SVM can be formulated as a quadratic optimization problem:
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