Tagged Questions

1
vote
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
votes
1answer
70 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 ...
0
votes
1answer
90 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 ...
0
votes
0answers
59 views

PCA prediction with centered vs. un-centered input data

I'm dealing spectral data. I want to classify 2 classes using principle components analysis. My PCA was constructed using prcomp(data, center=TRUE) in R. It works. ...
2
votes
3answers
249 views

Does PCA followed by LDA make sense?

This is a question about classification. I am a neuroscience student with little experience of classification methods and I'd be grateful for any advice about the best way to implement a linear ...
1
vote
3answers
142 views

Classifying handwritten digits using PCA

Classify handwritten digits using PCA. Use 200 digits for the train phase and 20 for the test. I have no idea how PCA works as a classification method. I've learned to use it as a dimension ...
2
votes
1answer
234 views

PCA before random forest classification

Does it make sense to do PCA before carrying out a Random Forest Classification? I'm dealing with high dimensional text data, and I want to do feature reduction to help avoid the curse of ...
3
votes
2answers
195 views

Classification on principal components

For my research I am doing classification on the dataset of three variables. I run unsupervised clustering (based on a histogram peak technique of cluster analysis)and the result I evaluated visually ...
5
votes
2answers
123 views

Claiming validity of a study's negative finding

For a disease that I am interested in, five or six published works of research have shown an association of changes in gene expression with presence of the disease. However, all these diagnostic ...
5
votes
2answers
594 views

PCA and random forests

For a recent Kaggle competition, I (manually) defined 10 additional features for my training set, which would then be used to train a random forests classifier. I decided to run PCA on the dataset ...
3
votes
4answers
525 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 ...
3
votes
2answers
168 views

Should I re-scale the PCA score before classification?

This question is about using PCA as a dimension reduction method before feeding the data into a classifier. It's a common procedure to use PCA for a data set which contains a large number of features, ...
6
votes
1answer
409 views

How to determine which factor levels are best?

DISCLAIMER: I don't have a lot of stats experience, so please don't laugh too hard if my question is trivial. I have run an experiment with 5 categorical factors. The factors have anywhere between 2 ...
1
vote
0answers
64 views

Comparing original variables with characteristic values of diagonalized variance-covariance matrix

If I have a reference data set comprising repeated measurements of 3 variables of a system in state $A$. Given new observations of these variables for a different system I would like to classify ...
3
votes
5answers
665 views

Does PCA have any advantages or usages in the frequency domain?

My question is about analysis of signals with PCA in the frequency domain. As frequency analysis offers a powerful tool for signal processing, does Principal Component Analysis (PCA) have any ...
8
votes
1answer
285 views

Correlating continuous clinical variables and gene expression data

In SVM (linear kernel) classification analyses of a data-set of gene expression (~400 variables/genes) for ~25 each of cases and controls, I find that the gene expression-based classifiers have very ...
3
votes
1answer
129 views

Creating groups with multi-dimensional data?

I'm trying to figure out the best way of creating groups in a dataset with many dimensions. I have 1000 measurements, and each measurement has 40 dimensions. The measurements are of neighborhoods with ...