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	<title>Comments on: Dimensionality reduction: Isomap</title>
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	<pubDate>Thu, 28 Aug 2008 07:25:03 +0000</pubDate>
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		<title>By: Matt&#8217;s blog &#187; Dimensionality reduction: comparison of different methods</title>
		<link>http://matt.eifelle.com/2008/01/25/dimensionality-reduction-isomap/#comment-449</link>
		<dc:creator>Matt&#8217;s blog &#187; Dimensionality reduction: comparison of different methods</dc:creator>
		<pubDate>Wed, 23 Apr 2008 07:16:59 +0000</pubDate>
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		<description>[...] already given some answers in one of my first tickets on manifold learning. Here I will give some more complete results on the quality of the dimensionality reduction [...]</description>
		<content:encoded><![CDATA[<p>[...] already given some answers in one of my first tickets on manifold learning. Here I will give some more complete results on the quality of the dimensionality reduction [...]</p>
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		<title>By: Matt&#8217;s blog &#187; Dimensionality reduction: explicit optimization of a cost function</title>
		<link>http://matt.eifelle.com/2008/01/25/dimensionality-reduction-isomap/#comment-18</link>
		<dc:creator>Matt&#8217;s blog &#187; Dimensionality reduction: explicit optimization of a cost function</dc:creator>
		<pubDate>Wed, 02 Apr 2008 07:16:08 +0000</pubDate>
		<guid isPermaLink="false">http://matt_temp.eifelle.com/item/13#comment-18</guid>
		<description>[...] on Euclidien distances, I implemented it with the approximated geodesic distances described in the Isomap ticket. The goal of this function is to add a weight (the inverse of the geodesic distance), leading to [...]</description>
		<content:encoded><![CDATA[<p>[...] on Euclidien distances, I implemented it with the approximated geodesic distances described in the Isomap ticket. The goal of this function is to add a weight (the inverse of the geodesic distance), leading to [...]</p>
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