/Dimensionality-Reduction

Playing around with some common Dimensionality Reduction Techniques

Primary LanguagePython

Dimensionality-Reduction

run: python Test_PCA.py

Includes implementation of PCA on a multivariate Gaussian example based on scatter matrix calculation and covariance matrix estimation

Tutorial link: https://www.cs.princeton.edu/picasso/mats/PCA-Tutorial-Intuition_jp.pdf

run: python PCA_scipy_test.py

scipy based PCA on same test example

run: python Plot_CV.py

test example on cross validation

run: python Nested_CV_test.py

test example on Nested Cross Validation technique

run: Baseline.py

Baseline classification on full rank dataset using svc

run: Classify.py

Implements PCA/KPCA on dataset, followed by svc. Stratified k-fold is used

run: PCA_complete.py

Implements dimensionality reduction on dataset followed by linear/Lasso/Ridge/ElasticNet/support vector regression

run: Test_PLSR.py

Test example on Partial Least Squares Regression

run: PLSR.py

Implements Partial Least Squares Regression(univariate case) on dataset