After vectoring the training and test sets, apply Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) and represent the features in the lower dimensional space (k <<<< 15). Then apply the SVM on both lower dimensional feature space and analysis the results.

Steps need to be considered:

1. Data pre-processing: apply the PCA and LDA to reduce the feature dimensionality.
2. Model implementation: train the Kernel-SVM method with new features generated using PCA and LDA
3. Testing sets preparation: apply the PCA and LDA on testing samples.
4. Evaluation: testing the models with independent test sets (20% samples).