python Programming Glossary: pca

http://stackoverflow.com/questions/13224362/pca-analysis-with-python

Analysis with Python I have a 26424 x 144 array and I want.. Python I have a 26424 x 144 array and I want to perform PCA analysis over it using Python. However there is no particular.. to achieve this task There are some sites which just do PCA according to their own there is no generalized way of doing..

Principal component analysis in Python

http://stackoverflow.com/questions/1730600/principal-component-analysis-in-python

in Python I'd like to use principal component analysis PCA for dimensionality reduction. Does numpy or scipy already have..

How to get the 1st Principal Component by PCA using Python?

http://stackoverflow.com/questions/17916837/how-to-get-the-1st-principal-component-by-pca-using-python

to get the 1st Principal Component by PCA using Python I have a set of 2D vectors presented in a n 2.. the data points in a regular list of lists type matrix. myPCA PCA dataMatrix # make a new PCA object from a numpy array object.. data points in a regular list of lists type matrix. myPCA PCA dataMatrix # make a new PCA object from a numpy array object..

Dimension Reduction in Categorical Data with missing values

http://stackoverflow.com/questions/2837850/dimension-reduction-in-categorical-data-with-missing-values

Similarly to what is done in Principal Component Analysis PCA and Factor Analysis the MCA solution can also be rotated to..

Kmeans without knowing the number of clusters?

http://stackoverflow.com/questions/6615665/kmeans-without-knowing-the-number-of-clusters

Otherwise you try a different k . Also you could do PCA principal component analysis to reduce your 50 dimensions to.. your 50 dimensions to some more tractable number. If a PCA run suggests that most of your variance is coming from say 4..

Calculating the percentage of variance measure for k-means?

http://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means

datasets from sklearn.decomposition import RandomizedPCA from matplotlib import pyplot as plt from matplotlib import.. data datasets.load_digits t data 'target' # perform PCA dimensionality reduction pca RandomizedPCA n_components 2 .fit.. # perform PCA dimensionality reduction pca RandomizedPCA n_components 2 .fit data 'data' X pca.transform data 'data'..