Clear Filters
Clear Filters

Using pca for features selections

22 views (last 30 days)
Bella on 22 Aug 2019
Answered: Greg Heath on 23 Aug 2019
I have 13 features from 100 breast thermal images, to detect breast cancer, which are (means, standard deviation, correlation, contrast, energy, entropy, skeweness, homogeneity, variance, smoothness, KURTOSIS, RMS and IDM) and I want to use them to train Ann for classification (benign or malignant). How could I use pca to get the best features? Should I normalize the values before? And when I apply pca I get coeff and score, should I use the score as the input of my Ann or is there an equation between coeff and my features to use for my Ann? Sorry for my long question but pca is still confusing me!!!

Answers (1)

Greg Heath
Greg Heath on 23 Aug 2019
PCA (Principal Coordinate Analysis) is a very useful method for regression (it ranks linear combinations of the original variables)
PLS (Principal Component Analysis) is a more useful method for classification (it ranks the original variables) !
I do not understand why it is not covered more extensively (if at all!) in the elementary statistics texts..
I have several posts in BOTH the NEWSGROUP and ANSWERS.
Hope this is helpful.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!