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	<title>Neurobot &#187; Dimensionality Reduction</title>
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	<link>http://neurobot.bio.auth.gr</link>
	<description>A computational neuroscience and neuroinformatics blog</description>
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		<title>Using ISOMAP algorithm for feature extraction in spike sorting</title>
		<link>http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting/</link>
		<comments>http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting/#comments</comments>
		<pubDate>Wed, 30 Jan 2013 20:02:43 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Sparse neurons]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3326</guid>
		<description><![CDATA[Background noise and spike overlap pose problems in contemporary spike-sorting strategies. The (non-linear) isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure and helps with recognising the involved neurons. To reproduce this tutorial in MATLAB you will need : 1. ISOMAP source code for MATLAB (for more information and updated version see here: http://isomap.stanford.edu) [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Book: &#8216;The Elements of  Statistical Learning&#8217;</title>
		<link>http://neurobot.bio.auth.gr/2012/book-the-elements-of-statistical-learning/</link>
		<comments>http://neurobot.bio.auth.gr/2012/book-the-elements-of-statistical-learning/#comments</comments>
		<pubDate>Fri, 09 Mar 2012 20:49:59 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3148</guid>
		<description><![CDATA[The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Download the book PDF (corrected 5th printing) here: http://www-stat.stanford.edu/~tibs/ElemStatLearn/]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2012/book-the-elements-of-statistical-learning/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Spike sorting based on noise-assisted semi-supervised learning methodologies</title>
		<link>http://neurobot.bio.auth.gr/2012/spike-sorting-based-on-noise-assisted-semi-supervised-learning-methodologies/</link>
		<comments>http://neurobot.bio.auth.gr/2012/spike-sorting-based-on-noise-assisted-semi-supervised-learning-methodologies/#comments</comments>
		<pubDate>Sun, 08 Jan 2012 15:23:11 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Noise]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3076</guid>
		<description><![CDATA[SAN 2011 abstract in Neuroscience Letters 2011, vol. 500(Suppl.), e32-33.- DOI: http://dx.doi.org/10.1016/j.neulet.2011.05.161 Also, see presentation in SAN 2011]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2012/spike-sorting-based-on-noise-assisted-semi-supervised-learning-methodologies/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Performance comparison of extracellular spike sorting algorithms for single-channel recordings</title>
		<link>http://neurobot.bio.auth.gr/2011/performance-comparison-of-extracellular-spike-sorting-algorithms-for-single-channel-recordings/</link>
		<comments>http://neurobot.bio.auth.gr/2011/performance-comparison-of-extracellular-spike-sorting-algorithms-for-single-channel-recordings/#comments</comments>
		<pubDate>Wed, 07 Dec 2011 09:20:23 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3050</guid>
		<description><![CDATA[A review and comparative analysis paper of contemporary algorithms in the spike sorting domain, published in Journal of Neuroscience Methods. Wild J et al. “Performance comparison of extracellular spike sorting algorithms for single-channel recordings“. Journal of Neuroscience Methods, 2011, vol.203 (2), pp. 369-76; doi:10.1016/j.jneumeth.2011.10.013]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/performance-comparison-of-extracellular-spike-sorting-algorithms-for-single-channel-recordings/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<item>
		<title>Nev2lkit enhanced demo</title>
		<link>http://neurobot.bio.auth.gr/2011/nev2lkit-enhanced-demo/</link>
		<comments>http://neurobot.bio.auth.gr/2011/nev2lkit-enhanced-demo/#comments</comments>
		<pubDate>Thu, 19 May 2011 17:19:40 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2962</guid>
		<description><![CDATA[An example of a spike sorting task using &#8216;nev2lkit enhanced&#8216; tool. Nev2lkit acts as a preprocessor for the extracellularly recorded data, extracting neural waveforms (i.e. spikes) from the continuous time series. Nev2lkit enhanced features user-customized optimization of the time-window used during the spike extraction procedure. Consequently, Nev2lkit employs Principal Component Analysis (PCA) in the spike [...]]]></description>
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		<slash:comments>0</slash:comments>
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		</item>
		<item>
		<title>NASS: an empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology</title>
		<link>http://neurobot.bio.auth.gr/2011/nass-an-empirical-approach-to-spike-sorting-with-overlap-resolution-based-on-a-hybrid-noise-assisted-methodology/</link>
		<comments>http://neurobot.bio.auth.gr/2011/nass-an-empirical-approach-to-spike-sorting-with-overlap-resolution-based-on-a-hybrid-noise-assisted-methodology/#comments</comments>
		<pubDate>Sat, 26 Feb 2011 07:39:05 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Noise]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2881</guid>
		<description><![CDATA[Background noise and spike overlap pose problems in contemporary spike-sorting strategies. In this paper, both issues are addressed by a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations. A recently introduced manifold learning technique, [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/nass-an-empirical-approach-to-spike-sorting-with-overlap-resolution-based-on-a-hybrid-noise-assisted-methodology/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Tutorial on Geometrical Data Analysis: Algorithms for Vectorial Pattern-Analysis</title>
		<link>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/#comments</comments>
		<pubDate>Tue, 28 Dec 2010 12:10:14 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Neural Networks]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2787</guid>
		<description><![CDATA[By Dr. Nikolaos A. Laskaris The term ‘‘pattern’’, currently, encompasses the notion of a variety of data-forms the machines have to tackle with. Despite the fact that in early days it was used mostly for pictorial information, i.e. 2D-signals, now the same term stands almost for any output from a data-source. For instance, any digital-signal can be [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Manifold learning examples</title>
		<link>http://neurobot.bio.auth.gr/2010/manifold-learning-examples/</link>
		<comments>http://neurobot.bio.auth.gr/2010/manifold-learning-examples/#comments</comments>
		<pubDate>Thu, 16 Sep 2010 12:32:11 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2440</guid>
		<description><![CDATA[A tutorial on PCA, LLE and ISOMAP by Dan Ventura (2008). You may access the tutorial here or visit the author&#8217;s &#8220;Advanced Neural Networks and Machine Learning&#8221; course home page.]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2010/manifold-learning-examples/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Global Geometric Framework for Nonlinear Dimensionality Reduction</title>
		<link>http://neurobot.bio.auth.gr/2010/a-global-geometric-framework-for-nonlinear-dimensionality-reduction/</link>
		<comments>http://neurobot.bio.auth.gr/2010/a-global-geometric-framework-for-nonlinear-dimensionality-reduction/#comments</comments>
		<pubDate>Thu, 16 Sep 2010 12:20:24 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2439</guid>
		<description><![CDATA[The classic Tenenbaum&#8216;s paper that introduces ISOMAP, a manifold learning approach featuring non-linear dimensionality reduction. J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323 You may access the full text of the document here, or visit the ISOMAP Homepage for further details, Matlab code and data sets.]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2010/a-global-geometric-framework-for-nonlinear-dimensionality-reduction/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>SigTool: A MATLAB-based environment for sharing laboratory-developed software to analyze biological signals</title>
		<link>http://neurobot.bio.auth.gr/2009/sigtool-a-matlab-based-environment-for-sharing-laboratory-developed-software-to-analyze-biological-signals/</link>
		<comments>http://neurobot.bio.auth.gr/2009/sigtool-a-matlab-based-environment-for-sharing-laboratory-developed-software-to-analyze-biological-signals/#comments</comments>
		<pubDate>Sat, 16 May 2009 21:38:25 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2316</guid>
		<description><![CDATA[Developed to run within MATLAB, sigTOOL provides a programming and analysis environment for processing neuroscience data. A graphical-user interface to this environment provides the end-user with a self-contained application for waveform and spike-train analysis. User-written extensions to this application can be added to the interface on-the-fly without the need to modify any of the existing [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2009/sigtool-a-matlab-based-environment-for-sharing-laboratory-developed-software-to-analyze-biological-signals/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>An enhanced version of nev2lkit</title>
		<link>http://neurobot.bio.auth.gr/2008/an-enhanced-version-of-nev2lkit/</link>
		<comments>http://neurobot.bio.auth.gr/2008/an-enhanced-version-of-nev2lkit/#comments</comments>
		<pubDate>Tue, 23 Sep 2008 16:32:39 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2236</guid>
		<description><![CDATA[Extracellular recordings of spontaneous nerve activity is a common practice for a number of electrophysiological experiments providing valuable information concerning peripheral and central nervous system physiology of vertebrates and invertebrates. Extracellular electrodes record voltage potentials representing the activity of an unknown number of activated axons which may serve different functions. It is generally assumed that [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2008/an-enhanced-version-of-nev2lkit/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Tutorial on Principal Components Analysis</title>
		<link>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-components-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-components-analysis/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 11:01:18 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1974</guid>
		<description><![CDATA[A tutorial on Principal Components Analysis Lindsay I Smith February 26, 2002 This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-components-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Tutorial on Principal Component Analysis</title>
		<link>http://neurobot.bio.auth.gr/2005/tutorial-on-principal-component-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2005/tutorial-on-principal-component-analysis/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 10:45:09 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1973</guid>
		<description><![CDATA[A theoretical Tutorial on Principal Component Analysis. by Javier R. Movellan. Read the full document Copyright 1997, 2003 Javier R. Movellan. This is an open source document. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/tutorial-on-principal-component-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Tutorial on Principal Component Analysis</title>
		<link>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-component-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-component-analysis/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 09:49:52 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1972</guid>
		<description><![CDATA[A Tutorial On Principal Component Analysis Derivation, Discussion and Singular Value Decomposition. Jon Shlens &#124; jonshlens@ucsd.edu 25 March 2003 &#124; Version 1 Principal component analysis (PCA) is a mainstay of modern data analysis &#8211; a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind [...]]]></description>
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		<slash:comments>0</slash:comments>
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