PCA basically is a projection of a higher-dimensional space into a lower dimensional space while preserving as much information as possible.

I wrote a blog post where I explain PCA via the projection of a 3D-teapot:

...onto a 2D-plane while preserving as much information as possible:

Details and full R-code can be found in the post:
http://blog.ephorie.de/intuition-for-principal-component-analysis-pca

PCA basically is a projection of a higher-dimensional space into a lower dimensional space while preserving as much information as possible.

I wrote a blog post where I explain PCA via the projection of a 3D-teapot...

...onto a 2D-plane while preserving as much information as possible:

Details and full R-code can be found in the post:
http://blog.ephorie.de/intuition-for-principal-component-analysis-pca

I just wrote a blog post where I explain PCA via the projection of a 3D-teapot:

...onto a 2D-plane via PCA:

Details and full R-code can be found in the post:
http://blog.ephorie.de/intuition-for-principal-component-analysis-pca

PCA basically is a projection of a higher-dimensional space into a lower dimensional space while preserving as much information as possible.

I wrote a blog post where I explain PCA via the projection of a 3D-teapot:

...onto a 2D-plane while preserving as much information as possible:

Details and full R-code can be found in the post:
http://blog.ephorie.de/intuition-for-principal-component-analysis-pca