Dimensionality reduction and clustering
Paul G. Geertsema · 2023
Why do we care about dimensionality reduction? The incremental information of a feature. Using principal component analysis to retain the maximum variation in a dataset while eliminating redundant information. An application of principal components to the MNIST data. An introduction to partial least squares and autoencoders. Clustering algorithms and how they differ from dimensionality reduction. K-means clustering and hierarchical clustering. An application of hierarchical clustering to financial trading strategies.