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. Few first principal components are often suffice to grasp nearly all multivariate variability of the ...
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14 views
Nonlinear PCA ( R package homals)
I'm using the NLPCA to reduce the dimensionality of nine variables (4 nominal / 3 ordinal /2 numeric) to obtain the object-scores to be used as dependent variable in a regression model.
I'm using the ...
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1answer
43 views
How to output varimax-rotated data in R?
I have ran PCA on 25 variables and selected the top 7 PCs using 'prcomp'.
prc <- prcomp(pollutions, center=T, scale=T)
I have then done varimax rotation on ...
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0answers
37 views
PCA on binary data (0's & 1's) -> what does it mean when a PC is correlated with the nr of 1's per subject?
I conducted a PCA on dichotomous variables (0's and 1's). The dataset consists of human subjects and a few thousand genetic variants, where the presence of a genetic variant is indicated with 0's and ...
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2answers
29 views
How many PCs should varimax rotation be applied to?
I have a list of 25 air pollutants many of which are strongly correlated. I was hoping to reduce down to a short list of eigenvectors which would each be composed of a small number of the pollutants.
...
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0answers
29 views
Using PCA to merge and grade correlated items
I have a real estates' condos sold dataset with the following fields
DOM: Date on the market
sellPct: Percentage difference between the original and final price.
other fields such as Exposure( ...
3
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0answers
35 views
Reducing the dimension of an embedding
Let $O \in \mathbb R^{p\times m}$ be a data matrix of observations.
Suppose we are given a model $\mu : \mathbb R^n \rightarrow \mathbb R^m$ which is able to approximately fit the observations. Fix ...
2
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1answer
63 views
Which transformation is better for a PCA?
I'm analyzing morpho-functional indices of forelimbs in a subterranean rodent (e.g. olecranon length / ulna length x 100) and I don't know how to treat data prior to a PCA. Which transformation is ...
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20 views
PCA with same cases in different periods of time
I am new user of principal component analysis (PCA) and I have a big doubt.
I have 32 observations with 45 variables and I know that I cannot use the simple PCA for this analysis (n < p). However, ...
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1answer
52 views
Singular Value Decomposition and PCR
Can anyone guide me to understand the relation between Singular Value Decomposition (SVD) and Principle Component Regression (PCR)? I know that we can construct the principle components (PCs) using ...
3
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4answers
192 views
How to perform dimension reduction after doing PCA in R?
I have a big dataset of +- 40000 observations, each containing 784 variables. Because this dataset is extremely large I want to perform a dimension reduction.
Now everywhere I read that I can use ...
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1answer
54 views
Plotting high dimensional data
I want to plot high dimensional data on x y plane. For that I know three methods: Principal component analysis (PCA), multidimensional scaling (MDS) and a method from spectral graph theory (using the ...
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0answers
30 views
PCA replicate data/index R
I have a set of date covering petrol prices.
My example has two columns where each row represents a sequential date.
...
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1answer
41 views
SAS and Principal Components Analysis
I'm new to SAS coding from SPSS point and click. I don't currently have SPSS, so SAS is my only option.
My question is: how do I get the percent variance explained and cumulative variance explained ...
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1answer
30 views
What's the logic/motivation behind pre-filtering data based on variance?
I am currently working with gene expression data where I have a number of genes (variables) measured over a number of samples. I could not make sense of the statistics and visualization suite we have, ...
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1answer
65 views
Kernel PCA (in R)
I am attempting to use the kernel PCA features in kernlab but am having trouble understanding the output. In particular, it's unclear what scale the results are in ...
1
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1answer
30 views
Validating a Varimax implementation?
I'm writing an implementation of factor analysis and I'm having trouble convincing myself (or even better, proving) that my varimax implementation is correct. What's the best way to prove that a ...
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0answers
32 views
Closed form Karhunen-Loeve/PCA expansion for gaussian/squared-exponential covariance
The Gaussian, or squared exponential covariance is $k_{SE}(s,t) = \exp \left\{ -\frac{1}{2l} (s - t)^2 \right\}$. It is a common covariance function used in Gaussian processes. The Karhunen-Loeve ...
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36 views
removing correlated variables from an NMDS ana
I am using nonmetric multidimensional scaling (NMDS) to to analyze benthic community data.
The ordinations will be based on abundance estimates of over 60 benthic invertebrate species at around 20 or ...
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0answers
20 views
Sparse PCA adjusted variance explained
In the original Sparse PCA paper
Sparse Principal Component Analysis
ZOU, HASTIE, TIBSHIRANI
they describe a way to compute the adjusted variance explained by computing QR decomposition of the Z ...
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1answer
62 views
vector fit interpretation NMDS
So a colleague and myself are using principal component analysis (PCA) or non metric multidimensional scaling (NMDS) to examine how environmental variables influence patterns in benthic community ...
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0answers
56 views
PCA on log transformed data
I am about to conduct two Principal Component Analyses (PCA) on species abundance data and species composition data. I have about 12 different locations where abundance data for over 50 invertebrate ...
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0answers
44 views
Why does pre-rotation affect PCA?
So I noticed the following phenomena:
I first generate a three-dimensional (x,y,z) multivariate normal distribution with no covariance between the variables.
I then generate a second distribution by ...
2
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0answers
53 views
PCA and varimax references?
After doing PCA it is common that the first component describes the largest part of variability. This is important in i.e. study of body measurements where it is commonly known (Jolliffe, 2002) that ...
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1answer
60 views
Principal component analysis (PCA) on long-tailed data
(1) When doing PCA, do you assume the variables to be bell-shaped? Say if I have a bunch of variables, some are bell-shaped but some have characteristic long (right) tails (highly skewed and ...
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1answer
182 views
PCA using princomp in MATLAB
I'm trying to do dimensionality reduction using matlab princomp, but i'm not sure i'm do it right.
here is the my code just for test, but I'm not sure that I'm doing projection right:
...
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0answers
58 views
Is anything wrong with this approach to PCA?
I'm working on an implementation of PCA that works on very large data sets.
Based on my understanding of the algorithm, the first step is to do an SVD of the input ...
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1answer
92 views
Dimensionality Reduction Algorithm for Large Dataset?
I have a reasonably large (5k variables x 120k cases) that I'd like to run a dimensionality reduction algorithm on. I tried doing a simple Factor Analysis on it in SPSS, but it (predictably) barfed on ...
3
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1answer
69 views
PCA before train/test split
I have a dataset for which I have multiple sets of binary labels. For each set of labels, I train a classifier, evaluating it by cross-validation.
I want to reduce dimensionality using PCA. My ...
3
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2answers
65 views
PCs scores from Correlation and Covariance matrices through matrix computations and prcomp
I'm want to get PCs scores through matrix approach. My calculated PCs scores for correlation matrix matches with prcomp results but the PCs scores for covariance ...
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0answers
65 views
Robust statistical methods to estimate relationship between variables
I have a dataset with measured co2 fluxes for 5 years and also the corresponding meteorological data (pressure, temperature, humidity etc.) and soil data (soil moisture, temperature). I was able to ...
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0answers
34 views
geographic distance and mahalanobis distance
I am trying to match individuals based on monthly consumption and geographic consumption in a dense metro area. Essentially I want to create treatment and control pairs with the having both geo ...
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0answers
37 views
What are “factorial coordinates”?
The concept of "factorial coordinates" seems to arise in PCA and clustering contexts, from what I've gleaned from web searches, but I can't find a definition.
I'm interested in repeating an analysis ...
3
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3answers
169 views
How does PCA improve the accuracy of a predictive model?
I've seen in a kaggle challenge about digit recognition someone who used PCA before decision tree or other techniques.
I thought it was just for compressing data but he aimed to improve his score.
...
2
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4answers
92 views
Grouping samples by clustering or PCA
If I have 5 binary variables with values for 100 observations to give me a 5x100 matrix.
...
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1answer
54 views
Interpretation of Scree plots and Boruta Outcomes
I have 37 features in my dataset. I used Boruta package in R and according to its analysis, all the features are "important" and should be retained. I examined this result of Boruta and found that if ...
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0answers
32 views
Confusion related to decorrelation of PCA
I have this confusion related to PCA. PCA assumes that the variables given have corresponding latent variables which are uncorrelated. What PCA does it project the data into the eigen space which ...
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0answers
33 views
PCA eigenvectors with opposite values [duplicate]
I am performing a PCA analysis in Matlab on a 110*7 dataset using some custom code. I am checking my code against Matlab's princomp and my eigenvectors are the same ...
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0answers
42 views
dudi.pca error in v*row.w: non-numeric argument to binary operator
I need to run a dudi.pca from the package ade4. I am running it on a species dataframe with the 1st column as ...
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1answer
89 views
Should PCA be performed before I do classification?
I have got a problem about doing a classification. I have got around 50 datasets. Each of them has 15 features.
I am trying to use these features to classify the 50 datasets to either 'Good' or ...
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0answers
14 views
How to get average two features from the front and next impressions of the current fingerprint impression?
I am working on a Fingerprint recognition scheme using Assembling Invariant Moments. At the time of feature extraction ROIs would be failed to acquire for computing the features, so we chose to ...
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0answers
40 views
PCA training and testing example
From previous tutorial, I have a training data set of 3 words (observations) and their features length of words and number of lines:
...
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1answer
71 views
principal components analysis is creating correlated axes with nested data
I'm trying to do a principal components analysis with the aim of turning my set of correlated variables into a set of uncorrelated ones (rather than dimension reduction). However, the data are nested, ...
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0answers
74 views
PCA alternatives
I've heard about alternatives to principal component analysis, they use improved methods such as decomposition over a non-orthogonal basis or penalizing the optimization problem of finding the highest ...
0
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0answers
33 views
PC analysis and regression-percentage of explained variance
my question is about the percentage of explained variance in principle component.
different codes in R software show different values for percentage of explained variance(PEV) with the obtained PEV ...
1
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2answers
103 views
PCA weights vs. standardized
I have a number of timeseries on which I want to apply a PCA (using matlab). These time series have very different variances.
My objective is:
1) all time series have the same weight for me, no one ...
1
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0answers
43 views
Variable reduction techniques on qualitative factor with several variables
Suppose that I want to regress the income level of a worker with qualitative variables such as eye color and the state that they live. Obviously, if I try to create a model with these variables, I ...
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0answers
60 views
Variation explained by single variable
I’m trying to find a way to measure how much a single variable ‘summarizes’ a full set of continuous variables. For instance, in a PCA the first principal component will explain a certain percentage ...
1
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0answers
75 views
Elementary question about PCA and orthogonal regression
This elementary question may have been asked and answered many times already, but my searching abilities are not up to finding it in the archives.
I have recordings of intensities of two fluorescent ...
5
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2answers
128 views
What can cause PCA to worsen results of a classifier?
I have a classifier that I'm doing cross-validation on, along with a hundred or so features that I'm doing forward selection on to find optimal combinations of features. I also compare this against ...
0
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1answer
103 views
PCA on returns or levels?
I am doing a PCA on a few economic indices (which I standardized) (24 timeseries). When doing the PCA on the values/level of the index, I get rather similar loadings on each index, which I think is ...
