Regularized principal manifolds
Many settings of unsupervised learning can be viewed as quantization problems - the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised learning. This setting turns out to be closely related to principal curves, the generative topographic map, and robust coding. We explore this connection in two ways: (1) we propose an algorithm for nding...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Smola_Regularized2001.pdf||711.87 kB||Adobe PDF|
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