We viewed Dr. Gilbert Strang's Linear Algebra lecture (#21) on EigenValue and EigenVectors through MIT OpenCourseware. Ina then ran through a brief tutorial on matlab for functions for calculating eigenvalues and eigenvectors. The tutorials referred to are as under:
1.http://mit.ocw.universia.net/14.452/s02/pdf/MatlabTut.pdf
2.http://amath.colorado.edu/computing/Matlab/Tutorial/LinAlgebra.html
Showing posts with label eigenvectors. Show all posts
Showing posts with label eigenvectors. Show all posts
Wednesday, November 5, 2008
Friday, October 31, 2008
31 October 2008
Today we concluded our introduction to linear algebra with a distribution of Jieping Ye's Basics of Lineal Algebra Matrices handout:
http://www.public.asu.edu/~jye02/CLASSES/Fall-2007/NOTES/Basics-Matrix.pdf
His other class materials are also available here:
http://www.public.asu.edu/~jye02/CLASSES/Fall-2007/index.htm
For next week, we will discuss Eigenvectors and Eigenvalues in the context of Principle Components Analysis with bioinformatics applications. Archana and Jang will locate 2-4 papers including overview information for PCA, ICA and NCA (just a little on independent and network component analysis). Ina will find Matlab tutorial information for eigenvalues, eigenvectors and PCA computation. Mike will find video lecture introductory materials for eigenvectors and eigenvalues.
On Tuesday we will watch and discuss the lecture and Ina will give a Matlab demo for computing eigenvalues and eigenvectors. On Thursday we will discuss the papers we've all been reading all week, and conclude with a tutorial computation of principle components from a biomedical dataset.
http://www.public.asu.edu/~jye02/CLASSES/Fall-2007/NOTES/Basics-Matrix.pdf
His other class materials are also available here:
http://www.public.asu.edu/~jye02/CLASSES/Fall-2007/index.htm
For next week, we will discuss Eigenvectors and Eigenvalues in the context of Principle Components Analysis with bioinformatics applications. Archana and Jang will locate 2-4 papers including overview information for PCA, ICA and NCA (just a little on independent and network component analysis). Ina will find Matlab tutorial information for eigenvalues, eigenvectors and PCA computation. Mike will find video lecture introductory materials for eigenvectors and eigenvalues.
On Tuesday we will watch and discuss the lecture and Ina will give a Matlab demo for computing eigenvalues and eigenvectors. On Thursday we will discuss the papers we've all been reading all week, and conclude with a tutorial computation of principle components from a biomedical dataset.
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