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How to Estimate the Regularization Parameter for Spectral Regression Discriminant Analysis and Its Kernel Version - IEEE Project 2014-2015

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  micansinfotech  +91 90036 28940 +91 94435 11725  MICANS INFOTECH , NO: 8 , 100 FEET ROAD,PONDICHERRY .   WWW.MICANSINFOTECH.COM ; MICANSINFOTECH@GMAIL.COM   +91 90036 28940; +91 94435 11725   IEEE Projects 100% WORKING CODE + DOCUMENTATION+ EXPLAINATION  –  BEST PRICE   LOW PRICE GUARANTEED How to Estimate the Regularization Parameter for Spectral Regression Discriminant Analysis and itsKernel Version? Abstract Spectral regression discriminant analysis (SRDA) has recently been  proposed as an efficient solution to large-scale subspace learning problems. There is a tunable regularization parameter in SRDA, which is critical to algorithm  performance. However, how to automatically set this parameter has not been well solved until now. So this regularization parameter was only set to be a constant in SRDA, which is obviously suboptimal. This paper proposes to automatically estimate the optimal regularization parameter of SRDA based on the perturbation linear discriminant analysis (PLDA). In addition, two parameter estimation methods for the kernel version of SRDA are also developed. One is derived from the method of optimal regularization parameter estimation for SRDA. The other is to utilize the kernel version of PLDA. Experiments on a number of publicly available databases demonstrate the effectiveness of the proposed methods for face recognition, spoken letter recognition, handwritten digit recognition, and text categorization. Existing System The existing regularization parameter estimation methods. In addition, our  proposed approaches for SRKDA are competitive to the best performance of SRKDA obtained existing methods, our approach is much more efficient and  micansinfotech  +91 90036 28940 +91 94435 11725  MICANS INFOTECH , NO: 8 , 100 FEET ROAD,PONDICHERRY .   WWW.MICANSINFOTECH.COM ; MICANSINFOTECH@GMAIL.COM   +91 90036 28940; +91 94435 11725   IEEE Projects 100% WORKING CODE + DOCUMENTATION+ EXPLAINATION  –  BEST PRICE   LOW PRICE GUARANTEED achieves higher accuracy in parameter estimation. The effectiveness and efficiency of our method are demonstrated in a number of applications including face recognition and text categorization. Proposed System This paper proposes to automatically estimate the optimal regularization  parameter of SRDA based on the perturbation linear discriminant analysis (PLDA). In addition, two parameter estimation methods for the kernel version of SRDA are also developed. One is derived from the method of optimal regularization  parameter estimation for SRDA. The other is to utilize the kernel version of PLDA. Experiments on a number of publicly available databases demonstrate the effectiveness of the proposed methods for face recognition, spoken letter recognition, handwritten digit recognition, and text categorization. Proposed the spectral regression discriminant analysis (SRDA), which can transform the srcinal discriminant analysis into a regression framework and achieve a much more efficient computation. With the rapid increasing of high-dimensional data in an age of big data, to estimate the regularization parameter of SRKDA. So this paper is motivated to propose another effective method to automatically estimate the regularization parameter for SRDA from the view of  perturbation linear discriminant analysis (PLDA)  micansinfotech  +91 90036 28940 +91 94435 11725  MICANS INFOTECH , NO: 8 , 100 FEET ROAD,PONDICHERRY .   WWW.MICANSINFOTECH.COM ; MICANSINFOTECH@GMAIL.COM   +91 90036 28940; +91 94435 11725   IEEE Projects 100% WORKING CODE + DOCUMENTATION+ EXPLAINATION  –  BEST PRICE   LOW PRICE GUARANTEED System Requirements Hardware Requirements ã   System : Pentium IV 2.4 GHz. ã   Hard Disk : 40 GB. ã   Floppy Drive : 1.44 Mb. ã   Monitor : 15 VGA Colour. ã   Mouse : Logitech. ã   Ram : 512 Mb.  Software Requirements ã   Operating system : - Windows 7. 32 bit ã   Coding Language : C#.net 4.0 ã   Data Base : SQL Server 2008  Conclusion  How to determine an appropriate regularization parameter α is a crucial  problem for SRDA and SRKDA. In order to solve this problem, three contributions were made in this paper. Firstly, it is the first time to discover and establish the strong relationship between SRDA and RLDA. Secondly, we propose an efficient method based on PLDA to estimate the optimal α of SRDA. Finally, we developed  micansinfotech  +91 90036 28940 +91 94435 11725  MICANS INFOTECH , NO: 8 , 100 FEET ROAD,PONDICHERRY .   WWW.MICANSINFOTECH.COM ; MICANSINFOTECH@GMAIL.COM   +91 90036 28940; +91 94435 11725   IEEE Projects 100% WORKING CODE + DOCUMENTATION+ EXPLAINATION  –  BEST PRICE   LOW PRICE GUARANTEED two methods to estimate the regularization parameter for SRKDA, which has not  been solved before. Experiments on different databases illustrate that our approach can effectively estimate the regularization parameter for SRDA and provide more accurate estimation than the existing regularization parameter estimation methods. In addition, our proposed approaches for SRKDA are competitive to the best  performance of SRKDA obtained by exhaustively searching.
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