Category "pca"

Use Index of the Loadings (IL) method to calculate the optimal number of principal components

I want to use Index of the Loadings method to calculate the optimal number of principal components using the index_of_loadings_matrix function. My dataframe was

PCA in R: Error in svd(x, nu=0, nv=k) : Infinite or missing values in 'x'

My dataframe contains about 26k rows with 129 variables. I've made sure all of the variables are numeric and do not have any NA values (used na.omit). Using the

PCA for Recurrent Neural Networks (LSTM) - Shall I use PCA for target variables too?

I have a seasonal timeseries dataset containing 3 target variables and n feature variables. I am trying to apply a PCA algorithm before feeding the data to a si

How to plot the pricipal vectors of each variable after performing PCA?

My question mainly comes from this post :https://stats.stackexchange.com/questions/53/pca-on-correlation-or-covariance In the article, the author plotted the v

How to perform PCA and UMAP dimensionality reduction with multiple images together

I have implemented PCA and UMAP dimensionality reduction for a single hyperspectral image. I didn't any problem. But now I have multiple hyperspectral images(mo

Retrieving PCA variables coefficients from PCA components

I am trying to explain a final score in the final assessment (predicted variable) via the scores of continuous assessment in seven subjects (predictive variable

What is the data type of X in pca.fit_transform(X)?

I got a word2vec model abuse_model trained by Gensim. I want to apply PCA and make a plot on CERTAIN words that I only care about (vs. all words in the model).

PCA on sklearn - how to interpret pca.components_

I ran PCA on a data frame with 10 features using this simple code: pca = PCA() fit = pca.fit(dfPca) The result of pca.explained_variance_ratio_ shows: array

Plot PCA loadings and loading in biplot in sklearn (like R's autoplot)

I saw this tutorial in R w/ autoplot. They plotted the loadings and loading labels: autoplot(prcomp(df), data = iris, colour = 'Species', loadings =

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