Tagged Questions

2
votes
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 ...
1
vote
0answers
53 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 ...
2
votes
1answer
58 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 ...
1
vote
0answers
37 views

PCA with “zero-spike” variables

I have a large number of predictors that are hypothesized to be important in determining my binary outcome variable (here's a bit more about my goal, the predictors and the outcome). The problem is ...
0
votes
0answers
102 views

Data transformation for PCA

I'd like to run a PCA (Principal Component Analysis) on my data, but I have a number of registries of some species as a dependent variable and 5 different variables of habitat (altitude, slope in ...
0
votes
2answers
91 views

Separating two complex-valued datasets that have been multiplied together

I have two complex-valued datasets, A and B that can be considered as vectors with the same number of elements. The datasets are multiplied together using complex point-by-point multiplication, ...
10
votes
2answers
2k views

“Normalizing” variables for SVD / PCA

Suppose we have $N$ measurable variables, $(a_1, a_2, \ldots, a_N)$, we do a number $M > N$ of measurements, and then wish to perform singular value decomposition on the results to find the axes of ...
4
votes
0answers
194 views

What, if any, dissimilarity is preserved in partial least squares (PLS)?

When we perform a principal components analysis (PCA) on a multivariate data set we are interested in finding orthogonal components that explain maximal variance in the data set. We can form a biplot ...
3
votes
1answer
1k views

Data transformation for Principal Components Analysis from different Likert scales

I have data from a survey comprised of several measures that used different Likert-type scaling (4-, 5-, and 6-point scales). I would like to run a principal components analysis using the data from ...
10
votes
5answers
1k views

SVD dimensionality reduction for time series of different length

I am using Singular Value Decomposition as a dimensionality reduction technique. Given N vectors of dimension D, the idea is to ...