Machine Learning Algorithms

Sparse PCA

scikit-learn provides different PCA variants that can solve particular problems. I do suggest reading the original documentation. However, I'd like to mention SparsePCA, which allows exploiting the natural sparsity of data while extracting principal components. If you think about the handwritten digits or other images that must be classified, their initial dimensionality can be quite high (a 10x10 image has 100 features). However, applying a standard PCA selects only the average most important features, assuming that every sample can be rebuilt using the same components. Simplifying, this is equivalent to:

On the other hand, we ...