dimensionality-reduction

I am trying to perform PCA reducing 900 dimensions to 10. So far I have: covariancex = cov(labels); [V, d] = eigs(covariancex, 40); pcatrain = (trainingData - repmat(mean(traingData), 699, 1)) * V; pcatest = (test - repmat(mean(trainingData), 225, 1)) * V; Where labels are 1x699 labels for chars (1-26). trainingData is 699x900, 900-dimensional data for the images of 699 chars. test is 225x900, 225 900-dimensional chars. Basically I want to reduce this down to 225x10 i.e. 10 dimensions but am kind of stuck at this point. The covariance is supposed to implemented in your trainingData : X =

I used a Dimensionality Reduction method (discussion here: Random projection algorithm pseudo code ) on a large dataset. After reducing the dimension from 1000 to 50, I get my new dataset where each sample looks like: [ 1751. -360. -2069. ..., 2694. -3295. -1764.] Now I am a bit confused, because I don't know what negative feature values supposed to mean. Is it okay to have negative features like this? Because before the reduction, each sample was like this: 3, 18, 18, 18, 126 ... Is it normal or am I doing something wrong? I guess you implemented the algorithm from this paper . As the

When I am trying to work with LDA from Scikit-Learn, it keeps only giving me one component, even though I am asking for more: >>> from sklearn.lda import LDA >>> x = np.random.randn(5,5) >>> y = [True, False, True, False, True] >>> for i in range(1,6): ... lda = LDA(n_components=i) ... model = lda.fit(x,y) ... model.transform(x) Gives /Users/orthogonal/virtualenvs/osxml/lib/python2.7/site-packages/sklearn/lda.py:161: UserWarning: Variables are collinear warnings.warn("Variables are collinear") array([[-0.12635305], [-1.09293574], [ 1.83978459], [-0.37521856], [-0.24527725]]) array([[-0

I saw this tutorial in R w/ autoplot . They plotted the loadings and loading labels: autoplot(prcomp(df), data = iris, colour = 'Species', loadings = TRUE, loadings.colour = 'blue', loadings.label = TRUE, loadings.label.size = 3) https://cran.r-project.org/web/packages/ggfortify/vignettes/plot_pca.html I prefer Python 3 w/ matplotlib, scikit-learn, and pandas for my data analysis. However, I don't know how to add these on? How can you plot these vectors w/ matplotlib ? I've been reading Recovering features names of explained_variance_ratio_ in PCA with sklearn but haven't figured it out yet

I am using PCA on 100 images. My training data is 442368x100 double matrix. 442368 are features and 100 is number of images. Here is my code for finding the eigenvector. [ rows, cols] = size(training); maxVec=rows; maxVec=min(maxVec,rows); train_mean=mean(training,2); A=training-train_mean*ones(1,cols); A=A'*A; [evec,eval]=eig(A); [eval ind] = sort(-1*diag(eval)); evec= evec(:, ind(1:100)); Now evec is an eigenvector matrix of order of 100x100 double and now I have got 100 eigenvectors sorted. Questions: Now, if I want to transform my testing data using above calculated eigenvectors then how