Questions tagged [svd]

Singular value decomposition (SVD) of a matrix $\mathbf{A}$ is given by $\mathbf{A} = \mathbf{USV}^\top$ where $\mathbf{U}$ and $\mathbf{V}$ are orthogonal matrices and $\mathbf{S}$ is a diagonal matrix.

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PCA of self generated data using orthogonal components itself, gives back inconsistent components

I was trying to study python API in sklearn for PCA to test if it can recover back the orthogonal vectors to generate data. Steps Used: Generate 3-D random orthogonal vectors. I take first two ...
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Correct use of S.V.D. regression on my data

All I(barley) know is electrical engineering. Only ever taken one basic stat class and a linear alg. class. So please use layman terms. I'm working on a wireless power transfer system. (Like those ...
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Computational advantage for soft-impute method over other methods

I am reading in the soft-imputing paper for low-rank-based matrix completion. They suggested another solution for $$\hat{Z} = \text{argmin}_Z\lVert X - Z \rVert_F^2 + \lambda \lVert Z \rVert_*$$ ...
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PCA with sample-specific prior information about principal components

Suppose I have some noisy dataset $\mathbf{X} \in \mathbb{R}^{N \times p}$ that I want to perform PCA on. Obtaining the (trimmed) SVD $\mathbf{X} = \mathbf{UDV}$ infers the q-dimensional principal ...
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Physical significance of non-negative factors of a matrix?

I was trying to make a recommender system using matrix factorization techniques on rating data. I came across 2 algorithms - SVD and NMF. While the basic difference is very clear , I was wondering ...
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How can I regularize data pre-processing parameter estimates (center, scale, rotation matrix)?

Suppose I have an $N×1$ vector $Y_{in}$ of response values an $N×P$ matrix of predictors $X_{in}$ whose individual columns exhibit significant correlation. Let's further suppose that this matrix $X$ ...
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Variable Selection : Removing Linear Dependency by SVD using the Condition number and then eliminating the variable causing multicollinearity

I am trying to perform regression with over 5000 feature variables(X) and I would like to eliminate multicollinearity. Incremental VIF computation is expensive. Incremental PCA works but I might lose ...
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28 views

Optimisation of reconstruction error for PCA

I am trying to understand the math behind the minimal reconstruction error for PCA models in this book. Given a data matrix $Y$ which has dimensions $D\times n$ and assuming that it is centered. The ...
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Why are the directions of eigenvectors in SVD and Eigen-Decomposition for PCA opposite? [duplicate]

As you may know, scikit-learn library utilizes singular value decomposition (SVD) of data matrix X to produce eigenvectors for PCA. I decided to code PCA by using ...
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Principal Component analysis - SVD U matrix projection

I have a bit mathematical question I am interested in. Principal component analysis (PCA) has mathematically multiple solutions. One way is to use SVD. I have prepared an example bellow. I am curious ...
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Fastest way to find Leading singular value and vector (power iteration, rsvd etc)

I want to know the fastest way to find out the leading singular value and vector of a large rectangular matrix. I have seen 2 suggestions and have questions on both of them : Power Method : For this ...
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Largest singular values

Given the positive semi-definite, symmetric matrix $A = bb^T + \sigma^2I$ where b is a column vector is it possible to find the singular values and singular vectors of the matrix analytically? I know ...
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Using SVD to write the least squares fitted vector

Elements of statistics p.66 Please I know the least squares solution for $\hat\beta = (X^TX)^{-1}X^Ty$ but I don't know how they were able to get $X\hat\beta= X(X^TX)^{-1}X^Ty = UU^Ty$ These are the ...
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How to compute the left singular eigenvector matrix (U) from the output of prcomp() for PCA in R?

I am examining the output of the prcomp() function in R for PCA in light of the singular value decomposition equation: $X = U \cdot \Sigma \cdot V^{T}$, where: $X$: ...
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Singular value decomposition used for dimensionality reduction in brain signal topographic data

I am trying to replicate the localizer method described in this paper (page 4). I am stuck on a step which I don't completely understand, and I would like your input and interpretation to progress. ...
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Physical interpretation of $U$ and $V$ matrices in SVD

I have a question about the physical interpretation of $U$ and $V$ matrices in SVD. I collect measurements at multiple devices across time are collected into an $m$ × $T$ matrix $M$, where m is the ...
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Kernel matrix decomposition

I had a look at the sklearn.kernel_approxiamtion.Nystroem implementation, which is also described in this post: Nystroem Method for Kernel Approximation Here, a ...
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Equal variance along left and right singular vectors?

Please confirm or reject my line of reasoning: Given SVD of $X$: $X_{NxP}=U_{NxP}D_{PxP}V_{PxP}'$, Variance along ith column vector of $U$ is given by $||X'u_i||^2=u'_iXX'u_i=u'_id_i^2u_i=d_i^2$, ...
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How to predict for test set when training a recommender by decomposing the utility matrix X=UV?

This probably sounds stupid but I don't get the workflow of building a recommending system by the utility matrix: X[i,j] = how much the ith user likes the jth object. For practical issues I refer to ...
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How does the Zeiger-McEwin & Kung algorithm work for fitting a sum of exponentials?

I am trying to understand this paper fit sum of exponentials but am having a bit of difficulty. Let me go through what I have understood so far. One has a certain time-series data set and want's to ...
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Question about the Proof of PCA in “Learning from Data” by Shwartz and Ben-David, P. 280-281

Does anyone know how to justify the red and blue line in the attached proof of PCA? Red line: $B \in \mathbb{R}^{ d \times n}$, arrange $B = [B_{j,1} | B_{j,2} | \cdots | B_{j,n}]$, then $B^\top B = \...
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What is so special about the least norm solution in case of an undetermined system of equations

In particular this is the go to approach in case of solving a least squares problem that lacks a unique solution, how does being the closest point to the origin among all the solutions make it any ...
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Principal components with and without centering [duplicate]

Suppose that I am given various samples of a vector random variable as the columns $v_1,v_2,\dots,v_n$ of a certain matrix $A$. Is there a relation between the SVD $A = USV^T$ the SVD $\hat{A} = \hat{...
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SVD for a complex data matrix — what is the meaning of the columns of $V$?

I've read this wonderful explanation of SVD, where the writer mentions that the columns of $V$ are the principal directions (Summary, #1). Is this also true when the data matrix $X$ is complex? If I'm ...
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Computing Pairwise Distances Through PCA or SVD

What should I do to reduce an mxn (m=17, n=650,000) matrix, where m are samples and n are features of these samples, into a matrix of pairwise distances (which I will then use to generate a dendrogram)...
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Which form of SVD is most commonly in stats/ML (e.g., PCA, least squares)?

SVM comes in several forms: e.g., the full form and the reduced forms https://en.wikipedia.org/wiki/Singular_value_decomposition#Reduced_SVDs. Is there one that is more commonly used in stats? If so, ...
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Human intuition behind SVD in case of recommendation system

This does not answer my question. I struggled very hard to understand the SVD from a linear-algebra point of view. But in some cases I failed to connect the dots. So, I started to see all the ...
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How vector projection works behind SVD?

I was reading a blog on mathematical intuition behind SVD. Here, author pointed out three information we get after vector decomposition. The directions of projection — the unit vectors (v₁ and v₂) ...
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SVD : Why right singular matrix is written as transpose

The SVD is always written as, A = U Σ V_Transpose The question is, Why is the right singular matrix written as V_Transpose? I mean lets say, W = V_Transpose and then write SVD as A = U Σ W SVD Image ...
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Relationship between the SVD and correlation matrices

I'm reading Data Driven Science and Engineering by Kutz and Brunton to understand more about the SVD. Consider $X = U\Sigma V$, $XX^*$, and $X^*X$ where $X \in \mathbb{R}^{m\times n} $ In particular, ...
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Penalized Canonical Correlation in R with PMA Module

I am trying to use sparse canonical correlation analysis as implemented in the R PMA package. I'm finding that the correlations output by the package seem slightly inconsistent with the ones you would ...
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216 views

How are eigenvalues/singular values related to variance (SVD/PCA)?

Let $X$ be a data matrix of size $n \times p$. Assume that $X$ is centered (column means subtracted). Then, the $p \times p$ covariance matrix is given by $$C = \frac{X^TX}{n-1}$$ Since $C$ is ...
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Symbolic Singular Value Decomposition? U,S,V as function of the elements of M [closed]

Suppose we want to compute the SVD of $\mathbf{M} = \begin{bmatrix} m_{11} & m_{12} & m_{13} \\ m_{21} & m_{22} & m_{23} \\ m_{31} & m_{32} & m_{33} \end{bmatrix}$ (...
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Variance of a principal component

Suppose that we have a SVD for our data matrix centered $X = UDV^T$. Then it is stated that the i-th principal component, $Xv_i$, has variance $\frac{d_i^2}{N}$. Consider these steps. $$ var(Xv_i) = ...
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Whitening on projection matrices

The projection matrix $P = I -xx^T\in \mathbf{R}^{d \times d}$ has a zero eigenvalue and eigenvalues equal to one with multiplicity $d-1$. Is it possible to apply whitening transform on $P$ taking ...
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How does partial least squares algorithm return more than one factor?

My understanding of PLS regression is that we find an eigen vector such that it maximises the covariance between X(matrix of independent variables) and Y(vector/matrix of dependent variable) i.e. find ...
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Use Matrix Factorization to predict probability of a recommendation system?

I have a dataset where I have a sparse utility matrix (user-product) with binary input: 1 if the user $i$ bought the product $j$, and 0 if it hasn't. However it has a different meaning on the test set,...
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In Probabilistic PCA, Where does the arbitrary orthogonal matrix(rotation matrix) come from?

I'm working on studying Probabilistic PCA based on the paper (Tipping & Bishop, 1999), I can follow the idea that the maximum likelihood function would reach the stationary point when the the ...
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Why is non-centered SVD accepted in LSA

In Latent Semantic Analysis (LSA) , we apply SVD to a term-document matrix $A$, then choose to ignore all but $k$ largest singular values. The term-document matrix is not centered, or normalised, ...
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Interpreting SVD on non-centered matrix

I have a very large, very sparse matrix $A \in \mathbb{N}^{n \times m}$ I'd like to perform SVD on. It is non-centered. When I center it to $A'$, I can't even fit it in memory (because $A'$ is in $\...
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Variability of K SVD components [duplicate]

Let's say I have a SVD of a matrix $A = U \Sigma V^T$, $A \in \mathbb{R}^{n \times m}$, and I'm using top-k components corresponding to $\sigma_1, ..\sigma_k$, the k largest values on the diagonal of $...
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Is there any relationship between SVD low rank approximation and DCT low frequency approximation?

Matrix can be approximated by low rank approximation using SVD by using major principle components. On the other hand, if we look at the frequency domain, matrix can also be approximated using DCT (...
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Does SVD provide the best low rank approximation for any matrix regardless of shape?

Wikipedia states (link below) that by the Eckart-Young-Mirsky theorem, the SVD provides the best low rank matrix approximation (on the basis of Frobenius norm of the error matrix) for any matrix A in ...
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What is finite precision arithmetic and how does it affect SVD when computed by computers?

Was reading the paper "DETECTING AND ASSESSING THE PROBLEMS CAUSED BY MULTICOLLINEARITY:A USE OF THE SINGULAR-VALUE DECOMPOSITION" by David Belsley and Virginia Klema. After performing SVD, while ...
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PCA with SVD exercice 23.5 understanding machine learning

In understanding machine learning Shai Sharev-Scwartz and Shai Ben-David exercice 23.5. I would like to use SVD to minimize : $$ \text{argmin}_{W \in \mathbb{R}^{n,d}, U \in \mathbb{R}^{d,n}}{\text{ }...
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Dimension reduction and get a dataset with the number of features that larger than the minimum of the numbers of samples and features

I'm trying to perform dimension reduction with SVD on an image dataset with the shape of roughly (3000,20000) as you see the number of each sample's features is way larger than the number of samples, ...
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How to project test data onto new space after using svd on trining data for dimension reduction [duplicate]

I'm working on a dataset of images and i was using the code below to apply dimension reduction to my data set and project the test data onto the new space: ...
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How to build a recommendation system for users outside of original model data?

I am creating a collaborative filtering model in Python to recommend possible movies a user may like. So, using typical methods in this field, I have a sparse matrix nxm with n users and m movies. ...
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211 views

Moore Penrose Pseudo-Inverse Fast Algorithm in R [closed]

I want to apply Moore Penrose Pseudo-Inverse on my matrix, which is a 20,000 * 20,000 symmetric matrix with rank 19,999. I found ginv() function from the ...
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1answer
60 views

Orthonormalization to use closed form Lasso solution

Given the Lasso problem $$ min_\beta (Y-X\beta)^\top(Y-X\beta) \quad s.t. \|\beta\|_1\leq\lambda, $$ and assuming that X is orthonormal such that $X^\top X=I$, we know that the closed form solution ...

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