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Old 06-02-2012, 03:27 PM
leonidr leonidr is offline
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Default K-means clustering for RBF centers

Why do we disregard the labels when we pick the centers?

I know that the first step is an example of unsupervised learning. But isn't the overall goal classification? And we measure the error of that classification based off of the labels. So, in practice, why would we disregard them in a typical supervised problem?

I know that one difficulty that immediately comes to mind is how do we combine (and/or map) the x and y into a space where the clustering can be performed easily; where the distance metric between two points weighs the x and the y appropriately (an open question I guess). But perhaps a naive approach such as appending the label (+-1) would have some benefit?


My apologies for asking a question outside the realm of the book, but I thought that it was relevant for the class. Any insight would be interesting.
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Old 06-03-2012, 06:25 AM
dudefromdayton dudefromdayton is offline
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Default Re: K-means clustering for RBF centers

Quick but very incomplete answer: by disregarding the labels, we don't have to charge for looking at the data. That is, we aren't snooping. This greatly reduces the dimensionality of the hypothesis set that we might ultimately formulate an answer from later. But I think there are other reasons, and I have to rush to cover an obligation.
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centers., k-means, rbf, unsupervised

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