Geometrical Data Analysis

Slides:



Advertisements
Similar presentations
Clustering Basic Concepts and Algorithms
Advertisements

Learning Trajectory Patterns by Clustering: Comparative Evaluation Group D.
Nonlinear Dimension Reduction Presenter: Xingwei Yang The powerpoint is organized from: 1.Ronald R. Coifman et al. (Yale University) 2. Jieping Ye, (Arizona.
Unsupervised learning
Uncertainty Representation. Gaussian Distribution variance Standard deviation.
One-Shot Multi-Set Non-rigid Feature-Spatial Matching
New Geometric Methods of Mixture Models for Interactive Visualization PIs: Jia Li, Xiaolong (Luke) Zhang, Bruce Lindsay Department of Statistics College.
USABILITY AND EVALUATION Motivations and Methods.
Locally Constraint Support Vector Clustering
Dimension reduction : PCA and Clustering Slides by Agnieszka Juncker and Chris Workman.
Dimension Reduction and Feature Selection Craig A. Struble, Ph.D. Department of Mathematics, Statistics, and Computer Science Marquette University.
Protein Homology Discovery Mixed bag of proteins Protein Homologies PHD Genes Database Open reading frame finder Proteins Database BLAST Clustering Protein.
Force Directed Algorithm Adel Alshayji 4/28/2005.
Ranking by Odds Ratio A Probability Model Approach let be a Boolean random variable: document d is relevant to query q otherwise Consider document d as.
Diffusion Geometries, and multiscale Harmonic Analysis on graphs and complex data sets. Multiscale diffusion geometries, “Ontologies and knowledge building”
Clustering Vertices of 3D Animated Meshes
Data Mining Mohammed J. Zaki.
Machine Learning Márk Horváth Morgan Stanley FID Institutional Securities.
Diffusion Maps and Spectral Clustering
Methods in Medical Image Analysis Statistics of Pattern Recognition: Classification and Clustering Some content provided by Milos Hauskrecht, University.
Machine Learning1 Machine Learning: Summary Greg Grudic CSCI-4830.
Teuvo Kohonen Dr. Eng., Emeritus Professor of the Academy of Finland; Academician Since the 1960s, Professor Kohonen has introduced several new concepts.
3 rd International Lab Meeting – Summer session th Edition of the International Summer School of the European Ph.D. on Social Representations and.
Conceptual Modelling and Hypothesis Formation Research Methods CPE 401 / 6002 / 6003 Professor Will Zimmerman.
Research Methods in Computational Informatics IST 501 Fall 2014 Dongwon Lee, Ph.D.
Non-Euclidean Example: The Unit Sphere. Differential Geometry Formal mathematical theory Work with small ‘patches’ –the ‘patches’ look Euclidean Do calculus.
Univ logo Fault Diagnosis for Power Transmission Line using Statistical Methods Yuanjun Guo Prof. Kang Li Queen’s University, Belfast UKACC PhD Presentation.
Multidimensional classification of burst triggers from LIGO S5 run Soma Mukherjee for the LSC Center for Gravitational Wave Astronomy University of Texas.
By Brian Lam and Vic Ciesielski RMIT University
Machine Learning Saarland University, SS 2007 Holger Bast Marjan Celikik Kevin Chang Stefan Funke Joachim Giesen Max-Planck-Institut für Informatik Saarbrücken,
Principal Component Analysis (PCA)
Intelligent Database Systems Lab Advisor : Dr. Hsu Graduate : Chien-Shing Chen Author : Jessica K. Ting Michael K. Ng Hongqiang Rong Joshua Z. Huang 國立雲林科技大學.
Math 285 Project Diffusion Maps Xiaoyan Chong Department of Mathematics and Statistics San Jose State University.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A survey of kernel and spectral methods for clustering.
Multidimensional classification analysis of kleine Welle triggers in LIGO S5 run Soma Mukherjee for the LSC University of Texas at Brownsville GWDAW12,
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Advisor : Dr. Hsu Graduate : Sheng-Hsuan Wang Author : Sanghamitra.
Outline ● Introduction – What is the problem ● Generate stochastic textures ● Improve realism ● High level approach - Don't just jump into details – Why.
Non-parametric Methods for Clustering Continuous and Categorical Data Steven X. Wang Dept. of Math. and Stat. York University May 13, 2010.
1 Review and Summary We have covered a LOT of material, spending more time and more detail on 2D image segmentation and analysis, but hopefully giving.
By Brian Lam and Vic Ciesielski RMIT University
The Role of Mathematics in Modelling Ecology
Machine Learning with Spark MLlib
Who am I? Work in Probabilistic Machine Learning Like to teach 
Machine Learning Clustering: K-means Supervised Learning
LECTURE 11: Advanced Discriminant Analysis
School of Computer Science & Engineering
Chapter 6 Classification and Prediction
Unsupervised Riemannian Clustering of Probability Density Functions
Lindita Camaj Associate professor
Soma Mukherjee for LIGO Science Collaboration
Lee, Jung-Woo Interdisciplinary Program in Cognitive Science
Invitation to Computer Science 5th Edition
به نام خدا Big Data and a New Look at Communication Networks Babak Khalaj Sharif University of Technology Department of Electrical Engineering.
Machine Learning Feature Creation and Selection
Intro to Machine Learning
Jianping Fan Dept of CS UNC-Charlotte
Learning with information of features
REMOTE SENSING Multispectral Image Classification
REMOTE SENSING Multispectral Image Classification
CSc4730/6730 Scientific Visualization
Feature Selection Analysis
Geometric and Intelligent Computing Laboratory
Dimension reduction : PCA and Clustering
Intro to Machine Learning
Visualization of Content Information in Networks using GlyphNet
Ernest Valveny Computer Vision Center
Introduction to Sensor Interpretation
Clusters and Densities
Introduction to Sensor Interpretation
Course project work tasks
Presentation transcript:

Geometrical Data Analysis Plato, 427-347 BC Geometrical Data Analysis

Algorithms for Geometrical Data Analysis N. Laskaris Geometrical Data Analysis

What isn’t Geometrical Data Analysis ? Statistical Data Analysis Hypothesis Driven methodologies A-priori (Top-Down) Data Modeling Parametric (model fitting) approaches Geometrical Data Analysis

Geometrical Data Analysis A little Motivation Geometrical Data Analysis

Information-Geometry vs. Informative - Geometry Geometrical Data Analysis

He is considered the father of spatial relations Roger Shepard (1929 - ) Prof. Emeritus of Social Science, Stanford University A cognitive scientist (Ph.D. in psychology 1955) and author of ‘‘Toward a Universal Law of Generalization for Psychological Science ’’ He is considered the father of spatial relations Geometrical Data Analysis

Geometrical Data Analysis Science, vol. 237, Sept.1987 Does psychological science have any hope of achieving a law that is comparable in generality (if not in predictive accuracy) to Neuton’s universal law of gravitation ? Geometrical Data Analysis

Geometrical Data Analysis Science, vol. 237, Sept.1987 Geometrical Data Analysis

Geometrical Data Analysis Michael Kirby Professor of Mathematics and Computer Science Graduate Program Director, Colorado State University An Empirical Approach to Dimensionality Reduction and the Study of Patterns Geometrical Data Analysis

‘‘Let the Data Speak for itself ’’ Sir Ronald Aylmer Fisher (1890-1962) ‘‘Let the Data Speak for itself ’’ Geometrical Data Analysis

Feature Extraction Distance measure Structure description Embedding in Feature-Space Structure description Geometrical Data Analysis

Partitional Clustering Outlier Geometrical Data Analysis

Hierarchical Clustering Geometrical Data Analysis

Graph-theoretic Clustering Geometrical Data Analysis

Geometrical Data Analysis Feature-selection Geometrical Data Analysis

Feature-normalization Geometrical Data Analysis

It’s an Ever Expanding field # 33 issue Geometrical Data Analysis

Geometrical Data Analysis

Geometrical Data Analysis Clustering Ensembles Geometrical Data Analysis

Geometrical Data Analysis Clustering Dynamics 1. Raw-data. 2. Feature-space. 3. Models Geometrical Data Analysis

Geometrical Data Analysis Randomization Kernel-based Clustering Geometrical Data Analysis