I have a original dataset with 70 samples, each sample with 96 features. The samples are labeled as 0 or 1. So I use linear discriminant analysis (LDA) to reduce the dimensionality of all the dataset, generating a samples with only one feature.
My results with all 96 features is 83% of accuraccy, with the projeted samples i have a 100% of accuracy. I'm using a svm for classification, with a split of 80% for training and 20% for test.
So my questions is : LDA is known as a supervised method of classification, but often used as a dimensionality reduction technique. The usage of LDA in all samples is doing a pre-training in the data? If yes, did I have to split the data before use LDA for reduction and later use the transformation to project the test data?