LFD Book Forum  

Go Back   LFD Book Forum > Course Discussions > Online LFD course > Homework 8

 
 
Thread Tools Display Modes
Prev Previous Post   Next Post Next
  #1  
Old 06-02-2012, 04:27 PM
leonidr leonidr is offline
Junior Member
 
Join Date: Apr 2012
Posts: 1
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.
Reply With Quote
 

Tags
centers., k-means, rbf, unsupervised

Thread Tools
Display Modes

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

Forum Jump


All times are GMT -7. The time now is 09:58 PM.


Powered by vBulletin® Version 3.8.3
Copyright ©2000 - 2021, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.