Applied Machine Learning 2019 - Lecture 14 - Dimensionality Reduction

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Principal Component Analysis, Linear Discriminant Analysis, Manifold Learning, T-SNE

Slides and more materials are on the class website:
  1. Andreas Mueller
  2. machine learning
  3. t-SNE
  4. PCA
  5. Principle Component Analysis
  6. Unsupervised Learning
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@17:00 we see that our principal components are linearly separable. Can I make a statement that our dataset will also be linearly separable in high dimensions? I know that if any 2 features of a high dimensional dataset are linearly separable, then my whole dataset will be linearly separable. But if 2 of my principal components (which are basically a combination of multiple features) are linearly separable, can I still make the same statement that my dataset is linearly separable?

ambujmittal