EE 381 V : Large Scale Learning Spring 2013 Lecture 11 — February 19
نویسنده
چکیده
The last two lectures focused on the algorithms and analysis of spectral clustering for Gaussian mixtures in the isotropic case, in which the i-th Gaussian has the distribution Xi ∼ N (μi, σ i I). Note that the covariance matrix is simply a multiple of the identity matrix, so each Gaussian is distributed spherically, as depicted in figure 11.1. Recall that in this case, we reduced dimension by projecting onto the subspace spanned by the top r components of the SVD.
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