svd Questions
3
Solved
Both SciPy and Numpy have built in functions for singular value decomposition (SVD). The commands are basically scipy.linalg.svd and numpy.linalg.svd. What is the difference between these two? Is a...
7
Solved
I am using truncated SVD from scikit-learn package.
In the definition of SVD, an original matrix A is approxmated as a product A ≈ UΣV* where U and V have orthonormal columns, and Σ is non-negativ...
Machismo asked 20/7, 2015 at 18:18
1
Solved
for the equation Ax = b, let A = USV.t, i need to calculate inverse of (S.T@S). I noticecd that using np.linalg.inv() and np.linalg.pinv() gives extremely different results. np.allclose() infact re...
Subsidize asked 2/4, 2022 at 18:1
2
Solved
I want to generate a rank 5 100x600 matrix in numpy with all the entries sampled from np.random.uniform(0, 20), so that all the entries will be uniformly distributed between [0, 20). What will be t...
Eley asked 18/1, 2022 at 4:47
0
I have a real square matrix X which I need to perform a Singular Value Decomposition on. Now, performing the operation
X=USV^T
as U and V are orthogonal, we know that det(X)=±det(S) and det(S) is n...
Terpene asked 5/11, 2021 at 14:17
1
Solved
I am using cusolverDnCgesvdjBatched function to calculate singular value decomposition (SVD) of multiple matrices, I use cuda-memcheck to check any memory issues, I am getting an error like this in...
1
I have used the SVD to find the rotation matrix between two sets of points. I know that R = Transpose(U) * V but I do not understand what U and V stand for and why this multiplication results in th...
Longobard asked 9/6, 2020 at 14:41
2
Solved
I was posting a question on similar subject, and encountered another more important question.
When I apply SVD to a matrix 'A' (code below) the output I get is the expected 2-D eigenvector matrice...
2
Solved
I was working on implementing a solver for sparse undetermined systems in Python (discussed here) and I was trying to rebuild the nullspace function that uses the standard numpy svd function (numpy...
Lore asked 16/6, 2018 at 2:41
2
Solved
I see that the documentation for both sklearn.decomposition.TruncatedSVD and scipy.sparse.linalg.svds mention that they both perform SVD for sparse matrices. What is the difference between them?
T...
Outstrip asked 9/9, 2013 at 20:54
1
The TruncatedSVD's explained variance ratio is not in descending order, unlike sklearn's PCA. I looked at the source code and it seems they use different way of calculating the explained vari...
Olette asked 9/2, 2016 at 18:6
3
I'm new to parallel programming using GPU so I apologize if the question is broad or vague. I'm aware there is some parallel SVD function in the CULA library, but what should be the strategy if I h...
Martlet asked 1/7, 2013 at 10:1
2
I am trying to apply PCA on huge sparse matrix, in the following link it says that randomizedPCA of sklearn can handle sparse matrix of scipy sparse format.
Apply PCA on very large sparse matrix
H...
Blubber asked 9/11, 2015 at 6:46
2
Solved
Problem
I have a set of equations with variables denoted with lowercase variables and constants with uppercase variables as such
A = a + b
B = c + d
C = a + b + c + d + e
I'm provided the in...
Asphyxiant asked 13/6, 2018 at 17:24
6
Solved
I'm trying to find the null space (solution space of Ax=0) of a given matrix. I've found two examples, but I can't seem to get either to work. Moreover, I can't understand what they're doing to get...
Grendel asked 4/5, 2011 at 19:56
2
Solved
I am trying to replicate an example in a paper called "An introduction to LSA":
An introduction to LSA
In the example they have the following term-document matrix:
And then they apply SVD and ...
Sacrilege asked 13/10, 2015 at 21:20
1
Solved
(Although there are a number of questions regarding how to best fit a plane to some 3D data on SO, I couldn't find an answer for this issue.)
Given N (x, y, z) points, I need the best fit plane
a...
1
Solved
I have a 3 million x 9 million sparse matrix with several billion non-zero entries. R and Python do not allow sparse matrices with more than MAXINT non-zero entries, thus why I found myself using J...
Rick asked 25/9, 2017 at 6:50
1
Solved
Can anybody tell me the differences between PCA(Principal component analysis ) , TruncatedSVD(Truncated singular value decomposition) and ICA(Independent component analysis) in detail?
Boastful asked 5/8, 2017 at 12:27
1
I have around 2-3 million products. Each product follows this structure
{
"sku": "Unique ID of Product ( String of 20 chars )"
"title":"Title of product eg Oneplus 5 - 6GB + 64GB ",
"brand":"B...
Monotheism asked 26/7, 2017 at 17:59
6
Solved
I know that principal component analysis does a SVD on a matrix and then generates an eigen value matrix. To select the principal components we have to take only the first few eigen values. Now, ho...
Improvisation asked 22/8, 2012 at 6:31
4
1
Solved
I'm trying to use the gesvd function from cuSOLVER which I found to be much slower than the svd function in MATLAB, for both cases using double array or gpuArray.
C++ code [using cuSolver]:
#inc...
3
Solved
All this time (specially in Netflix contest), I always come across this blog (or leaderboard forum) where they mention how by applying a simple SVD step on data helped them in reducing sparsity in ...
Eleonoraeleonore asked 6/3, 2012 at 19:0
2
Solved
I was doing some principal component analysis on my macbook running Microsoft R 3.3.0 when I got some strange results. Double checking with a colleague, I've realised that the output of the SVD fun...
Bloodandthunder asked 14/10, 2016 at 22:18
1 Next >
© 2022 - 2024 — McMap. All rights reserved.