M/EEG Data: What are we measuring? Jason Taylor MRC Cognition and Brain Sciences Unit (CBU) Cambridge Centre for Ageing and Neuroscience (CamCAN) 19 January 2011 | SPM M/EEG Course | Brussels [ Much stolen from James Kilner, Jérémie Mattout, Olaf Hauk ]. M/EEG: What are we measuring?.

ByAccuracy of PCA for Cancer detection applied to micro array data. by Nasser Abbasi Project supervisor: Dr C.H. Lee Mathematics department, CSUF. Goal of the study.

ByFinding Unusual Correlation Using Matrix Decompositions. David Skillicorn School of Computing, Queen’s University Math and CS, Royal Military College skill@cs.queensu.ca. The problem: Detect the footprint of terrorist presence/action in large datasets. The solution:

ByA Taste of Data Mining. Definition. “Data mining is the analysis of data to establish relationships and identify patterns.” practice.findlaw.com/glossary.html . Learning from data. Examples of Learning Problems. Digitized Image Zip Code

ByPaper No: 103 ICA 2007. Shifted Independent Component Analysis Morten Mørup, Kristoffer Hougaard Madsen and Lars Kai Hansen. The shift problem. Shift Invariant Subspace Analysis (SISA)

ByGene Regulation and Microarrays. Overview. A. Gene Expression and Regulation B. Measuring Gene Expression: Microarrays C. Finding Regulatory Motifs. A. Regulation of Gene Expression. Cells respond to environment. Various external messages. Heat. Responds to environmental conditions.

ByIndependent components analysis of starch deficient pgm mutants. GCB 2004 M. Scholz, Y. Gibon, M. Stitt, J. Selbig. Overview. Introduction Methods PCA – Principal Component Analysis ICA – Independent Component Analysis Kurtosis Results Summary. Introduction – techniques.

ByEE4-62 MLCV. Lecture 13-14 Face Recognition – Subspace/Manifold Learning . Tae-Kyun Kim. EE4-62 MLCV. Face Image Tagging and Retrieval. Face tagging at commercial weblogs Key issues User interaction for face tags Representation of a long- time accumulated data

ByMeasure Independence in Kernel Space. Presented by: Qiang Lou. References. I made slides based on following papers: F. Bach and M. Jordan. Kernel Independent Component Analysis. Journal of Machine Learning Research, 2002.

ByPrincipal Component Analysis and Independent Component Analysis in Neural Networks. David Gleich CS 152 – Neural Networks 11 December 2003. Outline. Review PCA and ICA Neural PCA Models Neural ICA Models Experiments and Results Implementations Summary/Conclusion Questions.

ByICA-based Clustering of Genes from Microarray Expression Data Su-In Lee 1 , Serafim Batzoglou 2 silee@stanford.ed, serafim@cs.stanford.edu 1 Department of Electrical Engineering, 2 Department of Computer Science, Stanford University. Ribosome Biosynthesis.

ByContent Based Image Retrieval. Natalia Vassilieva HP Labs Russia. Tutorial outline. Lecture 1 Introduction Applications Lecture 2 Performance measurement Visual perception Color features Lecture 3 Texture features Shape features Fusion methods Lecture 4 Segmentation

ByDurham Statistical Techniques Conference Summary II. Harrison B. Prosper Florida State University Workshop On Advanced Multivariate & Statistical Techniques Fermilab, 1 June 2002. Outline. Talks Probability Density Estimation and Optimizing S/B, Sherry Towers

ByThe Art of Digital Image processing. C. S. Tong Department of Mathematics Hong Kong Baptist University. Is the left center circle bigger?. No, they're both the same size. It's a spiral, right?. No, these are a bunch of independent circles.

ByData Mining Feature Selection. Data & Feature Reduction. Data reduction : Obtain a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results

ByReview of auto-encoders. Code. Sparsity constraint. Input decoding. Code energy. Code prediction. Decoding energy. Piotr Mirowski , Microsoft Bing London (Dirk Gorissen ) Computational Intelligence Unconference , 26 July 2014. Input. Outline. Deep learning concepts covered

ByIndependent Component Analysis. An Introduction. Zhen Wei, Li Jin, Yuxue Jin Department of Statistics Stanford University. Outline. Introduction History, Motivation and Problem Formulation Algorithms Stochastic Gradient Algorithm FastICA Ordering Algorithm Applications

BySubband-based Independent Component Analysis. Y. Qi, P.S. Krishnaprasad, and S.A. Shamma ECE Department University of Maryland, College Park. Subband-based ICA. Classical ICA and Applications Subband-based ICA Experimental Results Conclusions and Future Directions.

ByView Independent component analysis PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Independent component analysis PowerPoint presentations. You can view or download Independent component analysis presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.