Eigen Values - their Significance/interpretation?
362335
http://en.wikipedia.org/wiki/Eigen_value
620361
yes, it is true:\n\nIdeally, the Gaussian kernel is K(s,t) = exp(-(s2 + t2)/(2C2))/(2*Pi*C2)\n\nIn polar coordinates s = r*cos(theta), t = r*sin(theta), this expression is K(r, theta) = exp(-r2/2C2)/(2*Pi*C2)\n\nThe value of C determines the effective range of K; points (s,t) beyond a distance of about 3*C barely contribute to K.  K() is essentially flat beyond 3*C.  The quartic approximation capitalizes on this.  The only symmetric quartic polynomial equal to 1 at r=0, to 0 at r=3, and with derivative 0 at r=3 is q(r) = (1-(r/3)2)2\n\nTherefore Spatial Analyst's "Gaussian" kernel is a multiple of K(r, theta) = (1-(r/3C)2)2 suitably normalized to have an area of 1.  These values are very quick to calculate and so the density is quick to compute.
670173
you can detailed info abt this at http://www.mae.usu.edu/faculty/bsmith/EFDL/array/Array.html and http://in3.dem.ist.utl.pt/downloads/lxlaser2000/pdf/34_2.pdf
784392
Here is what i found.  I'm not sure how right this is, but you can check it out.  Good Luck with your testing.
735064
That sounds familiar... Think I'm working on it now! But I don't remember q. It is n in my book. In which case, q (if I'm thinking right) is the number of samples. Then again what's up with the ^'s? If np was > 10, then it meant that the distribution was approx. normal. You might be ahead of me right now...
139869
[ 0 0 ]\n[ 0 0 ] has determinate 0, thus it is not invertible.  All nxn matrices are diagnalizable, but the zero matrix is already in diagnol form, having zeros everywhere except the main diagnol.  It is just has diagnol entries (0, 0).  Thus I would say it is not non-diagnol.
130040
http://en.wikipedia.org/wiki/E_%28mathematical_constant%29\nfor the significance of e and how it is used, use this link
279951
It'd help to have  more detailed information. \nWhat does it mean that "pixels are independent?" \nAssuming that pixels are multivariate random variables, having n possible values for each color, the intensity of each component surely is correlated with that of adjacent pixels, I'd suppose.\nNow, what do you mean by "compression result"?\nPut more info. Your problem seems nice to tackle
978796
Study of data with respect to different spectral responses
9229
Given a vector space V, over a field F, and a linear function A:V -> V (that is for x,y in V and 'a' in F then  A(ax+y)=aA(x)+A(y) ), e in F is an eigenvalue of A if there is a non-zero vector v in V such that A(v)=ev. \n\nThe more specific meaning used in a linear algebra class is, if you have a NxN matrix A then e (where e is a rational/real/complex number) is an eigenvalue for A if there is an N dimensional vector v such that Av=ev.  \n\nEigenvalue comes from the German meaning proper value.
1155709
all odd numbers? but i dunno any other significance
1021968
http://en.wikipedia.org/wiki/Eigenstate
532980
I'm sorry but did you see this in My_Computer properties?\n\n\n\n--- ► λ ◄ \n\nN.B. I'm still a learner.
850193
Look here for the tables:\n\nhttp://wulfenite.fandm.edu/Data%20/Table_7b.html\n\nhttp://wulfenite.fandm.edu/Data%20/Table_7a.html\n\nThe values you need are in the second link.
962180
http://www.mcponline.org/cgi/content/abstract/3/4/327
455707
there's are so many different approach's....\n\ncitebase.eprints.org/cgi-bin/citations?id=oai:arXiv.org:nlin/0511015 \n\nhere's one site i'm sure you could link from there....
240066
First of all, let me know at what standard u need the answer.  Accordingly I will answer.  Essentially, SVD is used in Matrix algebra.  If you are below graduation, here is the simplest answer.\n\nSuppose you have two linear equations, with two unknowns, we will try to solve them as simultaneous equations.  similarly, if there are three quations with three unknowns, still we can attempt for solution.  Suppose there are mor equations than the number of unknowns, as in the case of randomised variables, that is four equations but three unknowns then the problem becomes serious.  we need to find the unknowns but satisfying all the equations.  Here we use this technique.  We try to find the solution of such cases using matrix inversion using Marquardt's algorithm or the solution of the characteristic equation leads to finding the eigen values.  So and so the theory continues.  That is why I asked at what level u need the answer?