# pca's questions - English 1answer

2.298 pca questions.

### Application of Factor Analysis to a table of count data

I am being asked to apply a statistical technique that I do not think is optimal. I have asked before, but was told my question was vague. I have re-worded it appropriately here. I am being asked to ...

### Can you use factor analysis on a table has count data? [on hold]

I am working on a project where my predecesor has been analyzing a table of rows by columns of count data. Brands represent the columns, and statements about those brands represent the rows. The cells ...

### PCA on continuous and binary data - further explained [duplicate]

I would like to perform a PCA on continuous data and binary data. Actually, I want to submit 8 continuous variables and 1 binary variable to a PCA. Is it a problem to include one binary variable ? ...

### Which analysis to use to discriminate morphometrics measurements from different species from 2 different environment?

So I have a dataset of measurements (lengths, surface areas, volumes...) from 3 species from 2 different environments, with 3 individuals per species. Can be summarised like that: ...

### 3 What exactly should be called “projection matrix” in the context of PCA?

0 answers, 28 views pca terminology linear-algebra
At the end of the PCA algorithm one gets a $D\times d$ matrix $U$ such that $z=U^Tx$ (here $x$ is $D$-dimensional and $z$ is $d$ dimensional with $d\leq D$). In multiple sources on the Web I found ...

### Signs in SPSS's PCA with rotations with the FACTOR algorithm

0 answers, 4 views pca spss factor-analysis svd eigenvalues
I am trying to reproduce the results of the PCA with rotations from SPSS in python. But there is some information I didn't find in their documentation. I am trying to do the PCA like in the FACTOR ...

### How can I write the resulted new representation of data, using LDA, from WEKA to an arff file? [closed]

Linear Discriminant Analysis (LDA) is a supervised dimensionality reduction algorithm, so from d dimensional data (input data) we want to obtain p new dimensions, where d>>p. It is the same principle ...

### 6 PCA is to CCA as ICA is to?

1 answers, 499 views pca canonical-correlation ica
PCA looks for factors in data that maximize explained variance. Canonical correlation analysis (CCA), as far as I understand, is like an PCA but looks for a factors that maximize cross covariance ...

### Compare principal components/differences between two groups

I have four groups of individuals which have a certain illness and are part of a clinical trial: Taking drug, illness gets better Taking drug, illness is not getting better Taking placebo, illness ...

### Procedure for Identifying Representative Documents

0 answers, 15 views pca kernel-trick word2vec
I have a large collection of documents, and would like to be able to select a subset of them that is representative of the whole. I have searched this question on here, Stack Overflow, and Google, ...

### SVM: Maximal number of components kernel PCA versus linear PCA

0 answers, 37 views pca svm kernel-trick
I'm comparing the Support Vector Machines (SVM) formulation of linear PCA with kernel PCA. I know that in linear PCA, the maximum number of principal components is equal to the dimension of the input ...

### Denoising with Kernel PCA, with handwritten digit denoising

0 answers, 27 views pca kernel-trick rbf-kernel
When using Kernel pca and denoising handwritten images, basically every number gets denoised very well, and just with 1 PC, we have a clean denoised image. Yet, the number 7 gets somewhat an extra ...

### 3 Understanding kernel PCA when the target space is infinite-dimensional

0 answers, 48 views pca kernel-trick
The PCA optimization problem is known as $$\max_{U \in \mathbb{R}^{d\times r}, U^TU = I} tr(U^T\Sigma U),$$ where $\Sigma$ is a covariance matrix of a dataset $\{x_1,\dots,x_n\} \subset \mathbb{R}^d$...

### 1 How to use Gower's Distance with clustering algorithms in Python

1 answers, 635 views machine-learning clustering pca k-means
I am trying to cluster by dataset with mixed features using k-means. As a distance metric, I am using Gower's Dissimilarity. I want to ask 2 things: -Is k-means an appropriate algorithm that can ...

### 2 Kernel function for use in Kernel-PCA given a known piecewise linear true data generating process

0 answers, 24 views pca kernel-trick
If I know that a multivariate dataset has a piecewise-linear data generating process with known knots (or breakpoints), then what is the appropriate kernel function to use in Kernel-PCA? For example, ...

### 4 Fundamental difference between PCA and FA?

1 answers, 54 views pca factor-analysis
According to this, the fundamental difference between PCA and FA can be illustrated via the following image: So, the direction of arrows changes. According to this answer and a few others: ...

### Pearson correlation after principal component analysis and varimax rotation

Is it possible (or does it make sense) to check for correlation after varimax rotation, since varimax assumes that there aren't any correlation between factors (or components)?

1 answers, 40 views pca terminology factor-analysis

### Frequency parameter of robust PCA for anomaly detection

1 answers, 422 views r pca anomaly-detection
I am using the R implementation of robust PCA here for anomaly detection. I have a vector of time series data, and a vector of dates. The algorithm works fine when the length of the vector is a ...

### 1 How can I calculate the standardized root mean square residual (SRMR) from the psych package in R?

0 answers, 13 views r pca factor-analysis
The psych package in R provides the root mean square of the residuals (RMSR) when using the principal (principal components analysis) or fa (factor analysis) functions. How could I calculate the ...

### feature selection and classification - train and test on the sample?

0 answers, 13 views pca feature-selection train vif ica
I have a dataset of 93 records and 45 radiomics variables from various CT scans. I wanted to check if age and sex could be classified by the variables so I made a new variable with both sex and age. I ...

### Using Principal Component Analysis (PCA) to construct a Financial Stress Index (FSI)

I am trying to construct a financial stress index. I have selected 12 variables that I use as indicators of financial market stress. These are all time series of daily data (VIX, credit spreads, etc.)....

### How to interpret linear regression coefficients on variables that have been transformed by PCA

0 answers, 12 views regression pca
Let's say I use PCA to reduce the dimensionality of my dataset before building a linear regression model. See R example: ...

### 3 Principal Component Analysis: Identifying Features that capture most variance of the Full Data Set

2 answers, 453 views python r scikit-learn correlation pca
I have a data set of 60 sensors. I wish to decrease the number of sensors used during an experiment, and use the remaining sensor data to predict the removed sensors. If I were to run principal ...

### Principal Component Analysis - Centering/Scaling Question

0 answers, 9 views pca centering
I was wondering if there is precedent for centering to the median and scaling to the median absolute deviation (MAD) (as opposed to arithmetic mean and standard deviation) prior to conducting PCA. I ...

### 6 Correlation between principal components

I have two matrices a, b of dimensions (100x500), (100x15000) and I am trying to find associations between sets of variables in both matrices. When I perform principal component analysis on matrix a,...

### Using a single principle component (PC) space to describe how a dataset changes across conditions

Given a design matrix that consists of N (>100) variables and J (>100) observations (the data, itself, is actual time-series): ...

### 1 How to plot High Dimensional supervised K-means on a 2D plot chart

I'm Having a ML problem where my data set contains 80 features labelled into 3 groups (0, 1, -1). I want to plot the data on a 2D surface to see how "close" (similar) data with ...

### 1 Apply statsmodels PCA to new data

1 answers, 15 views pca python statsmodels
I am working on a personal project, and I want to use Statsmodels' PCA on a dataset. The ultimate goal is to then perform a linear regression and evaluate its prediction. I know scikit-learn may be ...

### PCA with oblimin rotation: should I interpret component matrix, pattern matrix or structure matrix?

1 answers, 974 views pca spss cronbachs-alpha factor-rotation
I conducted a principal component analysis (PCA) with direct oblimin factor rotation in SPSS. Because by that time I didn't know any better, I used the COMPONENT MATRIX for interpretation. I added ...

I’m using Stata 12.0, and I’ve downloaded the polychoricpca command written by Stas Kolenikov, which I wanted to use with data that includes a mix of categorical ...

### Is a chi-square test for independence appropriate on a contingency table where one category is the unsupervised learning cluster?

I have a data set that has been partitioned into four clusters by executing a clustering algorithm that used principal components from a principal component analysis (PCA). I then make a contingency ...

### Can PCA be used for detecting multicollinearity?

1 answers, 39 views pca multicollinearity
The definition of multicollinearity is: Given a set of $N \times 1$ predictors $X = (x_1, x_2, \cdots, x_m)$, if $$x_j = \sum_{i \neq j}a_ix_i$$ then we say there is multicollinearity among the ...

### When applying PCA to a dataset consisting of regression coefficients, should one use PCA on correlation or on covariance?

This is a follow-up question from the post: PCA on correlation or covariance? The accepted answer quotes: You tend to use the covariance matrix when the variable scales are similar and the ...

### 1 Question about pca proof in deeplearning by ian goodfellow [duplicate]

0 answers, 11 views machine-learning pca optimization
Can anyone help me with this part? I don’t understand why d vector should be eigen vector of covariance matirx of X nor the generalization part

### 6 Does Neural Networks based classification need a dimension reduction?

3 answers, 11.457 views pca neural-networks
I am using a Neural Networks based classifier to run a classification for my data in n-dimensional. Then I thought it may be a good idea to run dimension reduction like PCA for my data at first, and ...

### Appropriate Statistical Analysis for Variable Reduction

0 answers, 11 views pca factor-analysis
I'm planning to conduct a study for clustering a set of observations. For the beginning, I'm planning to include more than 50 variables to my study. Therefore I must apply a relevant statistical ...

### -1 performing PCA on data sets of high variance [duplicate]

I am reading a documentation from Matlab (https://www.mathworks.com/help/stats/quality-of-life-in-u-s-cities.html#d119e60095) on performing PCA which makes this claim: When all variables are in ...

### principal component analysis with missing data

2 answers, 421 views clustering pca multivariate-analysis
for a prospective study of parameters affecting student's success in graduate school I am looking at a population of about 1500 med students. I have performed a cluster analysis (using Gower's ...

### 4 Evaluating an autoencoder: possible approaches?

Literature suggests that Antoencoders can be effective in dimensionality reduction, like PCA. PCA can be evaluated based on the variance of each principal component generated. How to do the same for ...

### 12 Can I do a PCA on repeated measures for data reduction?

I have 3 trials each on 87 animals in each of 2 contexts (some missing data; no missing data = 64 animals). Within a context, I have many specific measures (time to enter, number of times returning ...