Questions

1. Use k-means clustering for thresholding an image (use number of clusters=2) and compare the result with Otsu's.
2. Use scikit-learn's cluster.MeanShift() and mixture.GaussianMixture() functions to segment an image with mean shift and GMM-EM clustering methods, respectively—another two popular clustering algorithms.
3. Use Isomap (from sklearn.manifold) for non-linear dimension reduction and visualize 2-D projections. Is it better than linear dimension reduction with PCA? Repeat the exercise with TSNE (again from sklearn.manifold).
4. Write a Python program to show that the weighted linear combination of a few dominating eigenfaces indeed approximates a face.
5. Show that eigenfaces can also be used for naive face-detection (and recognition) and write Python code to implement this (hint—refer to this article: https://sandipanweb.wordpress.com/2018/01/06/eigenfaces-and-a-simple-face-detector-with-pca-svd-in-python/).
6. Use PCA to compute eigendigit-based vectors from the MNIST dataset (this is similar...