0
votes
0answers
16 views

Is subtracting the mean from PCA necessary when using an SVD result that is feature scaled?

I've applied SVD to the original data matrix and eliminated insignificant columns and rows from U and V^T respectively using the Sigma values. I multiplied together my optimized U, Sigma, and V^T ...
0
votes
0answers
14 views

Feature selection from wavelet transformation in R

I am new to wavelets. Currently, I am developing a prediction model using time series data. I am using the wavelets package in R. I am taking part of the time ...
1
vote
1answer
57 views

Number of components in PCA

I believe I have a problem understanding PCA: I would like to use this technique to reduce the number of features of my problem. I originally have 10,000 features and 500 samples. However, the use of ...
1
vote
0answers
59 views

Feature extraction for customer churn data

I have a customer churn data, and would be implementing algorithms (decision tree, logistic regression, segment analysis). I have doubt on feature extraction procedure though. The training sample has ...
2
votes
1answer
29 views

Selecting features manually and proving the rest are redundant

I'm working with a gesture dataset, where each gesture has a variable number of frames, and each frame has the 3d position of 20 joints, so that each gesture is represented by a matrix of size frames ...
0
votes
0answers
11 views

Error trying to reduce my data dimentions [duplicate]

I'm trying to produce a linear regression model, but I only have 25 observations and 34 predictors. I'm trying feature selection, ...
3
votes
2answers
66 views

Advice for interpolating a model

I'm new in Stack Exchange, so I hope no to be off topic. I'm also new in bioinformatics and I was asked to perform an analysis. Briefly, I have a dataset of 29 cell lines and the IC50 values of a test ...
0
votes
0answers
54 views

Is it possible to determine the set of variables contributing the most to first two principal components? [duplicate]

TL;DR: Is it possible to determine the set of features (covariates) that contribute the most to the first two principal components, and if so, how? Long version: Let's say I have a data table where ...
1
vote
1answer
99 views

Using Principal Components Analysis for feature selection

I have a dataset D made of m samples, and n features with n >> m. For each sample I have a score s which I would like to ...
0
votes
0answers
66 views

equivalent of PCA explained variance ratio for SVD ?

i am wondering if there is an equivalent of PCA explained variance ratio for SVD. What are the measures I can get to monitor the number of columns I keep after the SVD ? Are any of these metrics ...
2
votes
0answers
69 views

Confusion related to feature selection

Well my objective is to predict solar energy radiation at a particular location given some features like wind, temperature, humidity ... I have a total data for 10 years where I have the measurement ...
9
votes
3answers
331 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 ...
3
votes
0answers
168 views

Sensible to include ratio as a variable in logistic regression?

I'm creating a generalised linear regression using a binomial link function for two variables A and B. From looking at the data it appears that A/B may have discriminatory effect. Is it sensible to ...
17
votes
5answers
2k views

Detecting significant predictors out of 300 independent variables

In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
5
votes
2answers
4k views

Using PCA for feature selection

I'm new to feature selection and I was wondering how you would use PCA to perform feature selection. Does PCA compute a relative score for each input variable that you can use to filter out ...
3
votes
4answers
753 views

How to know when to stop reducing dimensions with PCA?

I'm using PCA to reduce dimensionality before I feed the data into a classifier. My bootstrap/cross-validation has shown a significant reduction in test error as a result of applying PCA and keeping ...
1
vote
3answers
2k views

Use of PCA analysis to select variables for a regression analysis

I have too many environmental variables to use in a multiple regression analysis. If I use all the variables the models are just too complex. The use of the PCA axes in the regression analysis was ...
1
vote
2answers
1k views

How to integrate principal components with GLM?

How would I integrate the output of a principal components analysis with a GLM (assuming the PCA is used for variable selection for the GLM)?
3
votes
1answer
441 views

Which are the most effective methods for selecting independent variables?

Some clustering algorithms require independence of variables but (especially working with real data) variables are often highly correlated. I have been suggested to apply a Principal Component ...