Spectral clustering is a multivariate statistical technique that uses the eigenvalues of a similarity matrix to reduce the dimensionality of data before clustering. It is commonly used in image segmentation for object categorization. The similarity matrix is an input and contains the relative similarity between each pair of points in the dataset.
Stanford University
Spring 2022
CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.
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