Spectral Clustering | Unsupervised Learning for Big Data

Traditional clustering algorithms, like k-means, struggle to cluster data that cannot be linearly separated. Spectral clustering escapes this problem, by using coordinates derived from diffusion maps. This lecture explores the derivation and application of this powerful clustering technique.
This is a part of a series of lectures from the Yale class "Unsupervised Learning for Big Data", taught by Professor Smita Krishnaswamy.
Unsupervised learning is perhaps the most beautiful and most frequently astonishing area of machine learning. It doesn't need to guzzle tons of labeled data to solve problems by brute force. Instead, it uses elegant mathematical principles to understand (in some sense) the data itself and the patterns underlying it.
Because this is a young field, there's no established textbook. The field of unsupervised learning is a collection of methods, and this course is an introduction to several of the most useful techniques, grounded in an intuitive understanding of the principles underlying them.
The tools from this class have been applied to an incredible range of problems, from molecular biology, to financial modeling, to medicine and even astrophysics. We're making these lectures publicly available in an effort to make it easier for anyone to make use of these powerful and elegant techniques in their own research.
To learn more about the Krishnaswamy Lab's work, visit krishnaswamylab.org

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Thank you Professor for making your lessons freely available on youtube! You rock!