Questions tagged [eigenvalues]
For questions involving calculation or interpretation of eigenvalues or eigenvectors.
400
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Generate a random covariance matrix with specified eigenspectra and diagonal elements?
I want to generate a random covariance matrix ($c \in \mathcal{R}^{n \times n} $) whose eigenspectra, i.e., $n$ eigenvalues $e_0 \in \mathcal{R}^{n\times 1}$ and diagonal elements $c_{ii} \,\, i=1 \,\,...
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Eigenvalues of Johansen Trace Test
I'm currently taking a course in time series and have been struggling with understanding the Johansen trace test. Specifically, the calculation of the eigenvalues for the Likelihood ratio statistic. ...
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EFA: Eigenvalues or Loadings after extraction (SPSS)
I've been doing a EFA with ML extraction and Promax rotation, whereby three factors were extracted. For reporting the results, I was wondering whether to use the 'Initial Eigenvalues' or the '...
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Is it possible to derive the expected sampled eigenvalues given a population correlation matrix?
My question is : Is it possible to derive the expected sampled eigenvalues given a population correlation matrix and a sample size $n$.
I am pondering about how behaves eigenvalues of correlation ...
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Popular symmetric positive definite matrices with explicitly known eigenvalues (at least largest eigenvalue)
I am looking for popular symmetric positive definite matrices with explicitly known eigenvalues (at least largest eigenvalue) arising as autocovariance matrices in time series (for example). In fact, ...
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Why is sum of squares equal to eigenvalue in PCA?
We fit a line or a hyperplane to a set of points. We project the points onto the hyperplane. The sum of squared distances of the projected points to origin is equal to the eigenvalue. Why is that?
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Positive semi definite matrix with negative eigenvalues?
From what I know, for any square real matrix A, a matrix generated with the following should be a positive semidefinite (PSD) matrix:
...
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Why in PCA the sum of the distances of from the principal component is the explained variance?
I am trying to understand why, when I calculate the principal components, summing the distances of the original points to the best fit line (eigenvector) equals the variance this PC explains?
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Eigenvector centrality comparison
Let's say we calculate the eigenvector centrality of the same set of nodes in different years (we have a network each year). Note that the eigenvector centrality is normalized in such a way that the ...
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Finding the eigenvalues of covariance matrix [duplicate]
Came across this as an interview question that I saw online: given a covariance matrix with diagonals being all 1 and the off-diagonals being c, what are its eigenvalues?
Going by the definition $Av = ...
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Show that the autocovariance matrix is positive definite
I've been working through the textbook Time Series Analysis and its Applications (R. H. Shumway & D. S. Stoffer 2ed). The topic I'm looking at is forecasting using ARMA models. The below assumes ...
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Different eigenvalues output running EFA in SPSS VS R
I am new to R. I ran exploratory factor analysis (EFA) in SPSS and in R. The SPSS output suggests 3 factors of eigenvalue>1 (8.78;1.5;1.05), while R indicates there is just 1 factor with eigenvalue&...
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The discrepancy of results of PCA via Eigendecomposition vs via SVD in Python with scipy.linalg [duplicate]
I recently learned about different methods of PCA. I decided to manually implement PCA in Python with Eigendecomposition of cov(X) and the Singular Value ...
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Is a bivariate copula relevant in this physics setting manifesting uniform univariate marginals--and, if so, how can it be constructed?
To quickly place our probabilistic (copula) question in its subject matter setting, we note that a fundamental concept in quantum theory is that of entanglement QuantumEntanglement.
The states of ...
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All eigenvalues less than 1 in factor analysis
Is it possible to have all eigenvalues less than 1 during factor analysis? then what can be concluded here? since a factor is considered a factor when the eigenvalue of the factor is greater than 1. ...
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How do I get the stationary distribution of a Markov chain matrix from SVD?
I have a matrix that represents a Markov chain.
...
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data projection on k - dime hyperplane (for eigenvector with largest eigenvalue) provides corrected representation
This is in reference to the text, outlier analysis by Charu C. Aggarwal:
The author mentions, the projection of data points on the k - dimensional hyperplane corresponding to the largest eigenvalues (...
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Eigenvalue decomposition
Why is the eigenvalue decomposition of the covariance matrix formula, $UΛU^T$ (assuming T means transpose) and not $UΛU^{-1}$? Where $U$ is eigenvectors and $Λ$ is a diagonal matrix consisting of ...
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Find first Principal Component (and loading) using a fast iterative algorithm without covariance matrix
I have a matrix $X$ and I would like to find its first principal component and the corresponding loadings. I would like to do this without computing the covariance matrix of $X$. How can I do so?
This ...
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Inequality on norm in terms of eigenvalue
I came across this inequality but not sure if it's true:
$|\lambda_{\text{min}}|^2\|\hat{\beta}-\beta\|^2_2\leq (\hat{\beta}-\beta)'\hat{\Sigma}(\hat{\beta}-\beta)$
where $\lambda_{\text{min}}$ is the ...
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Eigenvalues of time lagged covariance matrix - should be always real and positive?
Consider two random variables $X_t$ and $X_{t+\tau}$; where both come from random variable $X$ but are lagged by $\tau$. Assume that both are mean-free. If we want calculate covariance matrix of them, ...
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How to calculate eigen-decomposition for a positive kernel in R^2 using R
Suppose $K$ is a continuous positive semi-definite kernel on $\mathcal{T} = [0,1]\times[0,1] \subseteq \mathbb{R}^2$. By Mercer Theorem, there is a eigen-decomposition of $K$ such that
$$
K(x,y) = \...
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Negative Eigenvalues in EFA
Assuming a set of data meet all assumptions for EFA and we are doing a factor analysis (with the SMC used to define the shared variance), how do negative eigenvalues appear and what does that say ...
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Why is the first canonical direction equal to the left singular vector i.e. why is $w_1 = a = u_1$ in CCA (Canonical Correlation Analysis)?
I want to understand why the canonical direction $a$ is equal to the left singular value of $M = \Sigma^{-1/2}_X \Sigma_{X, Y} \Sigma^{-1/2}_Y$ and not $a = \sigma_1 u_1$.
My calculation tell me that ...
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Relation between PCA eigen values and data visualization
We have matrix data $X$ which is $n\times d$. We use the covariance matrix/ design matrix/ gram matrix $X^T X$ to perform least-squares/ PCA. I compute the eigen basis representation of said matrix
$$...
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number and size of eigenvectors in PCA
As I understand, the size of eigenvector produced in PCA should be min{n,N}, where N=number of samples and n=dimension of each sample (Right?). However, I have seen in couple of cases that this size ...
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Making sure that the design matrix is positive (-semi) definite
In bayesian linear regression, how to make sure that the design matrix produced by a neural network $ \Phi$ is positive definite? Because to computing the covariance matrix on the weight requires ...
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How does eigendecomposition form principal components? [duplicate]
I have a few questions regarding how specifically principal components are formed:
What is the relevance of the magnitudes of the covariance when it comes to eigendecomposition?
How does ...
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510
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Get accurate eigenvectors, when eigenvalues are minuscule
I have a symmetric matrix A. I'm not able to compute all the eigenvectors accurately, and I believe it is due to the last few eigenvalues for ...
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What are the metrics to assess the quality of a multiple correspondence analysis (MCA) model?
We are trying to implement a multiple correspondence analysis (MCA) model. I was looking for metrics to assess the quality of an MCA to evaluate our model. Sadly, I didn’t find much literature about ...
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How to interpret a PCA Biplot? [duplicate]
I constructed a PCA plot from a very high-dimensional dataset that contains features relating to fraud. After creating the PCA plot, I created a biplot with the features to see how they interact. The ...
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Why absolute value of eigenvalues are used in PCA or LDA?
In PCA and LDA techniques, eigenvectors with the $k$ largest eigenvalues give principal components. However, when selecting these eigenvalues, are they to be sorted by the absolute value (regardless ...
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What does it mean for Katz Centralities to "diverge"
In Mark Newman's Networks book, 2010 edition, page 173, he explains some mathematical details behind the Katz Centrality measure:
In matrix terms, Eq. (7.8) can be written x = αAx + β1, (7.9) where 1
...
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why do we need orthogonal basis for PCA? [duplicate]
In PCA, why do we map to the orthogonal basis? What is the important point that they should be orthogonal with each other?
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Are eigenvectors of PCA guaranteed to be orthonormal?
Are eigenvectors (principal components) of PCA orthonormal or only orthogonal ? Or only some of them are orthonormal or they are orthonormal if data were normalized before doing PCA ?
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Does the Cholesky decomposition of a covariance matrix lead to a lower triangular matrix with positive diagonals?
We know that an $N\times N$ covariance matrix $\Sigma$ is symmetric positive definite, and can be factorized using Cholesky decomposition as follows
\begin{equation}
\Sigma=LL'
\end{equation}
where $L$...
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Normalizing a custom Weight Shifted or Spiked Gaussian distribution
I have a custom weight shifted bivariate gaussian distribution that I wish to normalize. W is the weighted symmetric matrix that shifts the entire distribution and the λ below is the diagonal matrix ...
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Very low values of explained variance for the first Axes from PCoA
I am comparing different sites based on their floristic composition in R. Therefore, I have created a huge community datamatrix (presence/absence data) from 53 sites including over 1000 species. To ...
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In the rotational cost algorithm, are eigenvectors sorted according to eigenvalues, or according to absolute value of eigenvalues?
I am trying compute Rotational Cost, as defined in the 2004 paper "Self-Tuning Spectral Clustering"
by Zelnik-Manor and P. Perona (http://www.vision.caltech.edu/lihi/Publications/...
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Disambiguation of spectral clustering
I've read about spectral clustering, and feel like there are two related but different ways people use the words "spectral clustering".
Way 1
Spectral clustering as a pre-processing step ...
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How to check if my matrices are 'random' using Marchenko-Pastur?
For each $n=1,2,\dots,100$, I have an $r \times s$ integer-valued matrix $X(n)$ of increasing dimension as $n$ increases. That is, the number of rows $r$ and columns $s$ increases as $n$ increases.
...
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Is it possible that Fisher information matrix be indefinite? [duplicate]
I`m using the Newton-Raphson method for obtaining MLE for parameters for maximizing my objective function.
At each iteration, I want to check that is the Hessian matrix negative definite or not and I ...
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Why must the non-zero eigenvalues in the Johansen test be between 0 and 1?
Why are the non-zero eigenvalues in the matrix $\Pi$ in the Johansen test between 0 and 1? Why can't they be greater than 1 or less than zero? My lecturer just dropped in that's its because "the ...
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Is the QR Algorithm guaranteed to compute eigenvectors?
I'm writing some C++ matrix library for hobby.
For computing eigenvalues and eigenvectors, I referred the following "Francis double step QR algorithm":
In particular, page 82 of https://...
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Mercer's theorem and eigenfunctions
Consider a kernel $K$ which satisfies the conditions of Mercer's theorem. We know that $K(x,y) = \sum\limits_{i=1}^{+\infty}\lambda_ie_i(x)e_i(y)$ where $\lambda_i$ and $e_i$ are the eigenvalues/...
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How to translate eigenvectors and eigenvalues to the number of clusters in spectral clustering?
I have generated this output, where L is the Laplacian Matrix, D is the degree and A is the adjacency matrix:
I can see the eigenvalues and eigenvectors are returned. I am unsure how to interpret ...
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How to calculate the eigenvector?
I've been struggling to solve this math problem for two days...
So I calculated the mean of all samples (0,0).
Put it into the equation and got V as
\begin{array}
{rrr}
4 & 2 \\
2 & 2
\end{...
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Why is sum of squared PCA column loadings equal to corresponding Eigenvalue?
I'm wondering why or how is the sum of squared PCA loadings in the column equal to Eigenvalue. I understood that the sum of squared PCA loadings in the row is equal to 1 or 100% because original ...
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How to use principal components as inputs in hierarchical clustering analysis in R
For my statistical analysis I want to follow the steps of a paper I read.
I have a dataset in which each row corresponds to a dive carried out by a whale ('id' in table below) and the columns to the ...
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Why is there a discrepancy between the eigenvalues of the covariance matrix (PCA) and the eigenvalues of the kernel matrix (kernel PCA)?
I've done PCA on my data matrix $ \mathbf{X} $ which gives me i.a. the eigenvalues $ \lambda $ and eigenvectors $ v $ of the data covariance matrix $ C=\mathbf{X}^T \mathbf{X} $. I'm now extending my ...
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