similar to: using R's svd from outside R

I am reading the Mining of Massive Datasets Book by Rajaraman and Ullman. It has a good explanation of Recommendation System at Chapter 9. But what are the relationship between 1) SVD (Singular Decomposition) 2) UV-Decomposition 3) NMF (Non-negative Matrix Factorization) In particular, it seems 2) and 3) can be very similar. Is it right? Thanks. -- View this message in context:

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

Hello! I suppose this is more a matrix theory question than a question on R, but I will give it a try... I am using La.svd to compute the singular value decomposition (SVD) of a variance matrix, i.e., a symmetric nonnegative definite square matrix. Let S be my variance matrix, and S = U D V' be its SVD. In my numerical experiments I always got U = V. Is this necessarily the case? Or I might

Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

I am trying to perform Singular Value Decomposition (SVD) on a Term Document Matrix I created using the 'tm' package. Eventually I want to do a Latent Semantic Analysis (LSA). There are 5677 documents with 771 terms (the DTM is 771 x 5677). When I try to do the SVD, it runs out of memory. I am using a 12GB Dual core Machine with Windows XP and don't think I can increase the memory

Hi, I run a maanova analysis and found this message error: Error in ma.svd(X, 0, 0) : 0 extent dimensions I did a google search and found this: \item ma.svd: function to compute the sigular-value decomposition of a rectangular matrix by using LAPACK routines DEGSVD AND ZGESVD. \item fdr: function to calculate the adjusted P values for FDR control. I did a search for LAPACK and

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

--=====================_24736660==_ Content-Type: text/plain; charset="iso-8859-1"; format=flowed Content-Transfer-Encoding: quoted-printable SVD-Error on R 1.1.0 Windows 98 I get the following error applying svd on a positive definite matrix : > sk2 [,1] [,2] [,3] [,4] [,5] [1,] 1.0460139783 0.084356992 -2.810553e-04

Dear R list users, the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V. La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v). I noticed that the transpose of vt is not exactly u. Why is that? thank you for your attention and your help Stefano AVVISO IMPORTANTE: Questo messaggio di

Hi all, I have a time series that contains double seasonal components (48 and 336) and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and irregular component). As far as I know, the STL procedure for decomposing a series in R only allows one seasonal component, so I have tried decomposing the series twice. First,

Dear R Developers: Why is it that the singular value decomposition is used when running regression with arima, please? I've been looking for a reference for that but have come up empty so far. Thank you for any help. Sincerely, Erin Erin M. Hodgess, PhD Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: hodgesse@uhd.edu

Lately I'm getting this error quite a bit: Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd' I'm running R 2.5.0 on a 64 bit Intel machine running Fedora (8 I think). Maybe the 64 bit platform is more fragile about declaring convergence. I'm seeing way more of these errors than I ever have before. From R-Help I see that this issue comes up from time to

In summary.manova the qr decomposition of a NxN matrix is calculated and for some cases is giving me a rank < N. However, following suggestions of professor Ripley to calculate the rank of a Matrix On 7 Jun 2002, Brian Ripley wrote: > For a more reliable answer, look at the SVD > (function svd) and look at the > singular values. For example (from lda.default) X.s <-

Hi R users, I have a time series variable that is only available at a monthly level for 1 years that I need to decompose to a weekly time series level - can anyone recommend a R function that I can use to decompose this series? eg. if month1 = 1200 I would to decompose so that the sum of the weeks for month1 equals 1200, etc.. Many thanks in advance for any help. -- View this message in

Is there any way to give a "decent" title after I plot something generated by decompose? For example: # generate something with period 12 x <- rnorm(600) + sin(2 * pi * (1:600) / 12) # transform to a monthy time series y <- ts(x, frequency=12, start=c(1950,1)) # decompose z <- decompose(y) # plot plot(z) Now, the title is the ugly "Decomposition of additive time

I am a relative newcomer to both the R and C/C++ software worlds -- I'm taking a C Programming class currently. I noticed the other day that the C:\Program Files\R1_8_1\src\include\R_ext directory on my WinXP box has the header files BLAS.h Lapack.h Linpack.h RLapack.h I am interested in (perhaps) using one or more of these header files in a straight C program I'm working on in Visual