Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

Feature request: sklearn.decomposition.PCA using the covariance matrix

See original GitHub issue

Hi all, I am trying to compute PCA using sklearn.decomposition.PCA that seems that it uses correlation matrix I would like instead to use covariance matrix to compute the principal components.

I ask a question on StackOverflow two years ago but I get no answer.

I think it’s a very important feature see also this question on Crossvalidated StackExchange site.

Thanks and all the best! Giacomo

Issue Analytics

State:
Created 4 years ago
Reactions:1
Comments:7 (4 by maintainers)

Top GitHub Comments

1reaction

vicramrcommented, May 4, 2019

This stackexchange question has a discussion on the relationship between SVD and PCA. TL;DR if the features of your X matrix are mean-centered, you can find the principal components by finding either the eigenvectors of the covariance matrix, or the SVD of X. To my knowledge, the PCA algorithm involves first centering the features.

1reaction

css459commented, Apr 29, 2019

Hi I would like to claim this issue, and look into adding this to PCA if there is interest. Would there also be interest in implementing this as the Gramian Matrix?

Top Results From Across the Web

sklearn.decomposition.PCA — scikit-learn 1.2.0 documentation

Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space.

Principle Component Analysis (PCA) with Scikit-Learn - Python

PCA allows us to quantify the trade-offs between the number of features we utilize and the total variance explained by the data. PCA...

Feature Extraction using PCA - Python Example - Data Analytics

Python's sklearn. preprocessing StandardScaler class can be used for standardizing the dataset. Construct the covariance matrix: Once the data ...

Principal Component Analysis – How PCA algorithms works ...

Import Data · Step 1: Standardize each column · Step 2 Compute Covariance Matrix · Step 3: Compute Eigen values and Eigen Vectors...

Principal Components Analysis with Python (Sci-Kit Learn)

In a large dataset with many features, this is not practical, and it is ... The PCA of the data needs to be...