Principal component analysis is a technique to decompose an array of numerical data into a set of orthogonal vectors (uncorrelated linear combinations of the variables) called principal components. The first few principal components often suffice to grasp nearly all the multivariate variability of ...

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Weka Ranker principle component analysis

Hi I'm using the software WEKA to perform principle component analysis on a dataset, using the attribute evaluator "principle components" with the search method "ranker". Everything works fine but I ...
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Structure of semantic relationships using Latent Semantic Analysis

I am struggling to answer the below question: How would you describe the structure of semantic relationships among the terms from a document collection using principles of LSA? I understand that we ...
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15 views

First factor in Exploratory Factor Analysis and Principal Component Analysis

I am conducting an Exploratory Factor Analysis (EFA) and I was wondering if it is common or appropriate to say that the first factor is the strongest or most important of the model as it is explaining ...
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8 views

composite factor scores produced are automatically standardize? [on hold]

I had run a PCA to group 126 foods into smaller food groups, and it created 6-components (food groups). Are the composite factor scores produced for the 6-components by SPSS are automatically ...
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15 views

varimax rotation after PCA on covariance matrix [on hold]

I want to ask a pretty basic question regarding loadings rotation. I am wondering if it makes sense to do a varimax rotation after PCA using covariance matrix. I read somewhere that it is not ...
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21 views

PCA on fixed effects variables

I would like to run a panel logit model with fixed-effects on three indices, i.e. company, industry and time. The data set comprises around 1000 companies (index i) and 15 industries (j) over 6 years ...
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Reference for this claim: important features in data can be “hidden” in the higher PCA axes that are typically thrown out [duplicate]

I remember reading a paper a while ago that demonstrated some cases in which PCA would fail to capture important features of a data set in the first few principal components, but where those features ...
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19 views

Reproducing levels when PCA has been done on changes

I'm trying to do PCA on a time series which is not stationary. So I did the PCA on its daily differences, which are indeed stationary. I can then reproduce the daily changes using selected eigen ...
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10 views

Can I perform a PCA on species count differences instead on the species counts themselves?

I'm busy with the analysis of bird community change through time on a couple of sites and want to relate it to environmental covariates. I use the R-package vegan ...
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24 views

Eigen vector computing algo for pca [on hold]

I want to implement pca from scratch in c++. For this, I need to calculate the eigen vectors of a matrix. Of all the algo for calculating eigenvector, this is what I understood : This algo won't ...
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9 views

Using Princomp() in R [migrated]

After running Principal Component Analysis in R using princomp() and running summary() on the results I got a list of ...
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59 views

How to compute component or factor scores when the analysis is based on polychoric/tetrachoric correlations?

[This question is modified based on suggestion from @ttnphns] I am doing linear principal component analysis (PCA) based on polychoric correlations between the variables (rather than on native ...
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minimization of weighted frobenius norm for pca

So my problem is i like to derive pca solution as the maximum likelihood estimate for the true data.So basically i am assuming that my measured data has two component one is low rank component and ...
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1answer
19 views

Justification for variable reduction by removing predictors with near zero variance

I have a large number of variables that I'm trying to reduce, and I've stumbled on Kuhn's (2008) suggestion that I eliminate variables with zero or near-zero variance. This makes sense to me, it's ...
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7 views

Computing statistical significance for correlations between PCA scores

What would be the best way to statistically compare difference between component scores in PCA between different days? I used PCA for 10 days of my experiment. Since I have 5 principal components for ...
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24 views

PCA on nominal-ordinal data

I am trying to "decorrelate" two variables: one is binary categorical (cluster assignments) and the other one is ordinal (0 to 4 ratings). I have browsed around and came across Nonlinear principal ...
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1answer
39 views

PCA/MFA for (graphical) dimension reduction: what to do with very small explained variance?

I ran a Multiple Factor Analysis on a data set with 3,924 rows and 96 columns, of which six are (unordered) categorical, with 12-14 categories in each, and the rest are numeric, mean-centered and ...
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10 views

Minimum sample size required for sparse PCA

What is the minimum sample size that we need for filtering variables using sparse principal component analysis (sparse PCA, SPCA)?
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19 views

can we do pca analysis by binary characters? [duplicate]

I have a question about pca analysis. can we do it for binary characters? (for example for morphological characters). it seems that multistate characters are more appropriate for pca.could you please ...
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2answers
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Why do we need PCA whitening before feeding into autoencoder?

In the UFLDL tutorial, we saw that autoencoder can not compress data with uncorrelated random variables. 'If the input were completely random---say, each variable comes from an IID Gaussian ...
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How to choose the regularization parameter in ZCA whitening?

ZCA whitening can use regularization, as in $$ \tilde{X} = L\sqrt{(D + \epsilon)^{-1}}L^{-1}X, $$ where $LDL^\top$ is an eigendecomposition of the sample covariance matrix. What's a good choice for ...
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1answer
43 views

What does “varimax” mean in SPSS factor analysis?

In the rotation options of SPSS Factor Analysis, there is a rotation method named "Varimax". If I choose this option, does it mean the same orthogonal rotation techniques of Principal Component ...
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Estimating the opinion of a user by looking at opinions of other users

First of all, a bit of background: i am not a statistics expert but i am an enthusiast about data analysis. I have this list of "items" and for each item i have a list of "users" and the vote that ...
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Finding redundant variables

I have data of several variables (all numeric or continuous) on different subjects. I want to find out if some of these variables are highly correlated so that not all need to be determined. This will ...
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1answer
35 views

factor analysis with missing values

I have data on about 25 subjects and 30 variables with about 20 missing values. The data is missing at random. What will be the best approach to perform factor analysis. How is factor analysis versus ...
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18 views

Does biplot() function in R use rotations or loadings to plot arrows? [duplicate]

For following code performing principal component analysis: ...
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32 views

Establishing an empirical relationship among environmental properties using PCA and Multiple Regression

So, this post is a follow-up to a previous question of mine asked recently (Percentage of contribution of multiple factors to a single dependent variable), with more details on what I am trying to ...
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How to interpret this PCA biplot to determine which attributes to pick? [duplicate]

I'm running PCA on my dataset which can be found here. There are 6497 instances and 12 attributes with 13th column is the class (ranging from 3 - 9) for wine quality. I've read what PCA is supposed ...
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1answer
38 views

Interpreting standard deviation for PCA

I'm running PCA on my dataset using r and need some help interpreting the standard deviation results. Here are the results ...
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1answer
24 views

How to use factor analysis / PCA / regression for data having serial IV and DV?

I have data regarding effect of a food chemical on blood and urine levels as well as effect on blood sugar and cholesterol. So I have following variables: ...
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37 views

How to interpret PCA plots made using R [duplicate]

I'm using PCA for the first time and just experimenting with it. I used PCA on my dataset that can be found here ...
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1answer
76 views

Conclusions from output of a principal component analysis

I am trying to understand output of principal component analysis performed as follows: ...
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2answers
142 views

Applying PCA to test data for classification purposes

I've recently learned about the wonderful PCA and I've done the example outlined in scikit-learn documentation. I am interested to know how I can apply PCA to new data points for classification ...
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1answer
27 views

Condensing spatial time series data and spatial interpolation

I have spatio-temporal albedo (roughly, the 'reflectivity' of earth's surface) dataset, from NASA's MODIS satellite, for a 130 square kilometer area. The dataset contains raster files in the NetCDF ...
2
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1answer
70 views

Method for constructing large data set from smaller data set?

Is there a method for creating a large data set from a smaller one? I have a data set of anthropometric variables (e.g. stature, leg length, arm length and so on) So I have 7 variables and 1774 ...
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120 views

Loadings vs eigenvectors in PCA: when to use one or another?

In principal component analysis (PCA), we get eigenvectors (unit vectors) and eigenvalues. Now, let us define loadings as $$\text{Loadings} = \text{Eigenvectors} \cdot \sqrt{\text{Eigenvalues}}.$$ I ...
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19 views

Why is feature normalization important in PCA? [duplicate]

If feature normalization is not performed, does the algorithm give incorrect results or is it it inefficient or both?
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85 views

Rotation in PCA and Factor Analysis [duplicate]

I want to know what elements are (varimax-)rotated when I rotate after PCA and after Factor Analysis. Let’s assume a standardized data vector $X$ of dimension $N \times q$. In PCA, I have the ...
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1answer
27 views

Do the values in the second factor from a principal component analysis have to have different signs?

I am computing the principle component matrix of a financial database and to obtain the second factor I extrapolate the second vector. So far, it's easy, but I wonder, do the signs of the second ...
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1answer
61 views

Relation between variance of eigenvalues and the effectiveness of PCA on the data

If the covariance matrix has eigenvalues $$\lambda_1 \ge \lambda_2 \ldots \ge \lambda_d > 0,$$ why is the variance of the eigenvalues, $$\sigma^2=\frac{1}{d}\sum_{i=1}^d (\lambda_i-\bar ...
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1answer
59 views

Factor analysis to remove noise

Performing factor analysis/PCA to remove potential hidden latent variables from high dimensional data is extremely useful to remove confounding/noise/measurement error and batch effects. However, ...
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33 views

How to account for repeated measurements and unequal sample sizes in RDA

I have a question going in the same direction as this one: Restricted Permutations However, I have a dataset of multiple Locations from which I have samples of sometimes 5 sometimes only 3 years. Not ...
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1answer
45 views

Why is the weight vector in PLS constrained to be of unit length?

In the SIMPLS formulation of partial least squares (PLS) regression, the weights are constrained to have length of 1, $$r_a^Tr_a = 1,$$ where $a$ represents a latent component (from $1$ to $A$). This ...
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1answer
51 views

Interpretation of interaction between a PC and a continuous predictor on a logical response

I'm using lme4 in R to test the effect of various continuous explanatory variables, some of which I've corrected for their collinearity using PCA, on a logical response variable. My optimal model ...
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PCA - more than 2 principal components for >80% variance

I have analyzed some datasets using prcomp and some of my data is nice and amenable to PCA. But the summary of one set is showing that at least 6 components are needed to cover 80% of the variance. ...
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21 views

Help with interpreting PCA loadings

I know that there are already a few questions on PCA loadings on this site, however after reading them I still have some problems in understanding the interpretation. I have 22 days of data and I did ...
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41 views

How to use the weights from principal component analysis (PCA) to build an composite indicator

I'm working on the construction of a composite index based on five sub-indicators. The method I used is Principal Components Analysis (PCA). I visited Jeromy Anglim's blog post calculating composite ...
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19 views

PCA using prcomp and princomp in R [duplicate]

I have been trying to do Principal Component Analysis (PCA) via R. The data set is available at https://www.dropbox.com/s/s3jstl8pu1e1xcp/Cars.csv?dl=0 I tried to do PCA via 2 different methods - ...
3
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2answers
55 views

What is the relation between singular correlation matrix and PCA?

Can anyone kindly give me some information about the statement (last sentence) at the end of below definition. What does it mean by "It can be used when a correlation matrix is singular"? This quote ...
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2answers
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Is dimensionality reduction almost always useful for classification?

Is singular value decomposition almost always useful in practice for enhancing the predicative power of a trained classification model? E.x. A dataset for classification has 20,000 features. Run SVD ...