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Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

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![laplacian_matrix](https://user-images.githubusercontent.com/16658498/59164486-eb7b4380-8ad2-11e9-977d-f997501688dc.PNG) ![3d_bunny_resize](https://user-images.githubusercontent.com/16658498/59167559-60607480-8af7-11e9-9b61-4dc4abe13b80.png)

![lle](https://user-images.githubusercontent.com/16658498/56095416-5c6ef800-5ea2-11e9-8ce0-5da013aee4e8.gif)

![KleinDual](https://user-images.githubusercontent.com/16658498/55211718-3e737900-51bb-11e9-8a4a-37ecf83c9ab0.gif)

In part 4 about LLE, you define the weight matrix $W$ as a matrix that is $\mathbb{R}^{k \times n}$. However, $W$ is actually a sparse $n \times n$ where, for...