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	<title>De Rerum Natura &#187; Compressed Sensing</title>
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	<link>http://www.corrada.com/blog</link>
	<description>Randomness, entropy, pattern matching, maps, geometry, knots, and scientific readings</description>
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		<title>Robust voting in uncertain environments</title>
		<link>http://www.corrada.com/blog/2008/11/17/robust-voting-in-uncertain-environments/</link>
		<comments>http://www.corrada.com/blog/2008/11/17/robust-voting-in-uncertain-environments/#comments</comments>
		<pubDate>Mon, 17 Nov 2008 05:00:15 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[precision error application]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=83</guid>
		<description><![CDATA[Combining the judgments of different recognizers is always better than using the best one alone. This observation is universal in machine learning realms. But it seldom gets used in practice. Why?
For one, it costs more to implement. Instead of one recognizer, you must deploy several. Computing cycles grow linearly with the number of recognizers if [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/11/17/robust-voting-in-uncertain-environments/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Positive and negative precision error correlations, real or not?</title>
		<link>http://www.corrada.com/blog/2008/05/07/positive-and-negative-precision-error-correlations-real-or-not/</link>
		<comments>http://www.corrada.com/blog/2008/05/07/positive-and-negative-precision-error-correlations-real-or-not/#comments</comments>
		<pubDate>Thu, 08 May 2008 02:27:16 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=78</guid>
		<description><![CDATA[

All the experiments we have been carrying out with precision error have, so far, been with real data. Because of this, we do not have &#8220;ground truth&#8221; to determine if the reconstruction is correct. That changed today.
Synthetic experiments are a well-known device for studying models or algorithms. By artificially creating data where one knows exactly [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/05/07/positive-and-negative-precision-error-correlations-real-or-not/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>ICML accepts precision error via L1 minimization paper</title>
		<link>http://www.corrada.com/blog/2008/04/09/icml-accepts-precision-error-via-l1-minimization-paper/</link>
		<comments>http://www.corrada.com/blog/2008/04/09/icml-accepts-precision-error-via-l1-minimization-paper/#comments</comments>
		<pubDate>Wed, 09 Apr 2008 10:48:02 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Fourier Analysis]]></category>
		<category><![CDATA[Group Theory]]></category>
		<category><![CDATA[MathML]]></category>
		<category><![CDATA[Mathematics]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[Scientific Readings]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=69</guid>
		<description><![CDATA[Our technical report on how to recover precision error estimates with &ell; 1 -minimization has been accepted by the 2008 International Conference on Machine Learning.
The paper originally got three anonymous reviews. Two were positive, one strongly negative. In our response to the reviews, we agreed with the general criticism by the reviewers that one experimental [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/04/09/icml-accepts-precision-error-via-l1-minimization-paper/feed/</wfw:commentRss>
		<slash:comments>2</slash:comments>
		</item>
		<item>
		<title>Student answers versus random answers</title>
		<link>http://www.corrada.com/blog/2008/03/31/student-answers-versus-random-answers/</link>
		<comments>http://www.corrada.com/blog/2008/03/31/student-answers-versus-random-answers/#comments</comments>
		<pubDate>Mon, 31 Mar 2008 17:34:43 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[test assessment]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=65</guid>
		<description><![CDATA[An interesting baseline for thinking about precision error is to consider the case of uniformly random answers. The student may be completely ignorant: you gave a college level test to kindergarten kids. Your questions were so hard or so incomprehensible (think Chris Kattan&#8217;s mumbling character giving a &#8220;uupp-uizzz&#8221; (pop-quiz) to this students) that students are [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/03/31/student-answers-versus-random-answers/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Random faster than systematic</title>
		<link>http://www.corrada.com/blog/2008/03/24/random-faster-than-systematic/</link>
		<comments>http://www.corrada.com/blog/2008/03/24/random-faster-than-systematic/#comments</comments>
		<pubDate>Mon, 24 Mar 2008 10:40:07 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Randomness]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/03/24/random-faster-than-systematic/</guid>
		<description><![CDATA[I am writing a Mathematica program to produce the precision error signal and reconstruction matrix for an arbitrary number of models. The maximum number I had tried before was ten models because it corresponded to the number of maps we have for the 29 Palms dataset.
My first try consisted of systematically creating all possible permutations [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/03/24/random-faster-than-systematic/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>X-raying the geometric precision error of DEMs with Fourier analysis</title>
		<link>http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/</link>
		<comments>http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/#comments</comments>
		<pubDate>Sat, 02 Feb 2008 18:10:32 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Fourier Analysis]]></category>
		<category><![CDATA[Group Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[DEMs]]></category>
		<category><![CDATA[symmetry group]]></category>
		<category><![CDATA[theory of geometric errors]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/</guid>
		<description><![CDATA[In a previous post I mentioned a way of Fourier analyzing the geometric precision error of DEMs. Today I realized that the scheme I proposed can only account for part of the error signal. The approach I proposed is correct but it can only capture one particular aspect of the total error. The simplest way [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Error covariance matrices as images</title>
		<link>http://www.corrada.com/blog/2008/02/02/error-covariance-matrices-as-images/</link>
		<comments>http://www.corrada.com/blog/2008/02/02/error-covariance-matrices-as-images/#comments</comments>
		<pubDate>Sat, 02 Feb 2008 17:29:30 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Maps]]></category>
		<category><![CDATA[Mathematics]]></category>
		<category><![CDATA[covariance matrix]]></category>
		<category><![CDATA[DEMs]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/02/02/error-covariance-matrices-as-images/</guid>
		<description><![CDATA[I submitted my paper on autonomous precision error estimation in 3-D models to the 2008 International Conference on Machine Learning yesterday. One week early, too, a first for me! The format for the paper is the standard double column format and this makes it very hard to have complex equations in the paper. One mathematical [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/02/02/error-covariance-matrices-as-images/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Decreasing precision errors with randomness</title>
		<link>http://www.corrada.com/blog/2008/01/26/decreasing-precision-errors-with-randomness/</link>
		<comments>http://www.corrada.com/blog/2008/01/26/decreasing-precision-errors-with-randomness/#comments</comments>
		<pubDate>Sat, 26 Jan 2008 10:58:34 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Maps]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[DEMs]]></category>
		<category><![CDATA[geometric precision error]]></category>
		<category><![CDATA[sparsity]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=28</guid>
		<description><![CDATA[If I was to rate the things I have learned from computer science, I would place the algorithmic use of randomness right at the top. The uses of randomness in computations is too vast to start a list here. Check out Probability and Computing: Randomized Algorithms and Probabilistic Analysis for many examples. I want to [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/01/26/decreasing-precision-errors-with-randomness/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>No data is wasted</title>
		<link>http://www.corrada.com/blog/2008/01/22/no-data-is-wasted/</link>
		<comments>http://www.corrada.com/blog/2008/01/22/no-data-is-wasted/#comments</comments>
		<pubDate>Wed, 23 Jan 2008 00:31:23 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Maps]]></category>
		<category><![CDATA[DEMs]]></category>
		<category><![CDATA[Howard Schultz]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=27</guid>
		<description><![CDATA[Compressed sensing caught my attention last year. I was doing a literature search on the Internet to see if anyone else had discussed the autonomous difference equations that Howard Schultz and I had devised to measure the precision errors in Digital Elevation Models (DEMs). One of the basic tenets of compressed sensing is that since [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/01/22/no-data-is-wasted/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Digital Elevation Model errors are a sparse signal!</title>
		<link>http://www.corrada.com/blog/2008/01/02/digital-elevation-model-errors-are-a-sparse-signal/</link>
		<comments>http://www.corrada.com/blog/2008/01/02/digital-elevation-model-errors-are-a-sparse-signal/#comments</comments>
		<pubDate>Thu, 03 Jan 2008 01:25:19 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Maps]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=26</guid>
		<description><![CDATA[I have spoken in previous blogs of how the Terrest system developed by Howard Schultz exploits the asymmetry of computer stereo matching algorithms to produce two Digital Elevation Models (DEMs) from a pair of aerial photographs. This seems like a kind of trickery to many who are exposed to this feature of Terrest since the [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/01/02/digital-elevation-model-errors-are-a-sparse-signal/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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