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pca

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01 Jun 2009		Principal Component Analysis (PCA) in MATLAB This is a demonstration of how one can use PCA to classify a 2D data set. Author: Siamak Faridani	pca, classification, principal component a..., segmentation	137	3	4.33333 4.3 | 4 ratings

Files Matching AristoTELE's Watch List					View all 1 - 2 of 2
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
29 Apr 2008		Fourier Mellin Image Registration An implementation of Fourier Mellin Phase Correlation for Image Registration Author: Robinson Laundon	geometric transformat..., image registration, image registration fo...	59	8	5.0 5.0 | 2 ratings
12 Oct 2005		Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering. Author: Michael Kleder	basic kalman filter, kalman filter, discrete filter, linear, imtransform, filter analysis	659	87	4.4375 4.4 | 81 ratings

Comments and Ratings Matching AristoTELE's Watch List			1-5 of 127
Date	File	Comment by	Comments	Rating
08 Feb 2012	Fourier Mellin Image Registration An implementation of Fourier Mellin Phase Correlation for Image Registration	lama riad	what does 1 and 2 mean in size(I,1),size(I,2)??????	Comment only
05 Aug 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	greg	Nicely documented. As a practicing engineer I would never use the implementation shown. This is the standard covariance form of the Kalman filter. In operation the statement s.P = s.P - K*s.H*s.P; causes significant issues. s.P needs to always be positive definite but with rounding this will tend to violate this assmption making the Kalman filter 'blow up' over time or with poorly conditioned data. The alternative is to process in the square root domain where the P matrix is expressed as a P=Psr'*Psr. Therefore the resuting matrix must always be positive definite and does not have this issue. An added advantage is the precision is doubled processing in this domain and can be similar to the input data resolution rather than 2x the number of bits to provide comparable precision. For more information see 'Linear estimation' Kailath, Sayed, Hassibi or 'adaptive filter thoery' Haykin IMHO one should always to filtering in the SR domain. There are also advantages of processing with separated real/imag data rather then complex if the underling data is complex.	Comment only
22 Jul 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	prokash paul	nice doc	5
16 Jul 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	lotfi	good !	Comment only
27 Jun 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Edgar lobachevskiy	excellent!! Thank you!!	5