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AU2001218647A1 - Non-linear data mapping and dimensionality reduction system - Google Patents

Non-linear data mapping and dimensionality reduction system

Info

Publication number
AU2001218647A1
AU2001218647A1 AU2001218647A AU1864701A AU2001218647A1 AU 2001218647 A1 AU2001218647 A1 AU 2001218647A1 AU 2001218647 A AU2001218647 A AU 2001218647A AU 1864701 A AU1864701 A AU 1864701A AU 2001218647 A1 AU2001218647 A1 AU 2001218647A1
Authority
AU
Australia
Prior art keywords
data
dimensional
code vectors
grid
organizing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
AU2001218647A
Inventor
Jose Maria Carazo Garcia
Kieko Kochi
Roberto Domingo Pascual-Marqui
Alberto Domingo Pascual-Montano
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
KEY FOUNDATION FOR BRAIN-MIND RESEARCH
Consejo Superior de Investigaciones Cientificas CSIC
Original Assignee
KEY FOUNDATION FOR BRAIN MIND
Consejo Superior de Investigaciones Cientificas CSIC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by KEY FOUNDATION FOR BRAIN MIND, Consejo Superior de Investigaciones Cientificas CSIC filed Critical KEY FOUNDATION FOR BRAIN MIND
Publication of AU2001218647A1 publication Critical patent/AU2001218647A1/en
Abandoned legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2137—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Fuzzy Systems (AREA)
  • Databases & Information Systems (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Automation & Control Theory (AREA)
  • Algebra (AREA)
  • Geometry (AREA)
  • Computer Graphics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Electrically Operated Instructional Devices (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

Abstract

This is a system for organizing n-dimensional data onto a lower dimensionality space in a non-linear and non-supervised manner. The types of methods presented here are usually known as self-organizing maps and are similar but not identical to the well known Kohonen self-organizing maps. The basic idea is a combination of data clustering and smooth projection thereof into a lower dimensional space (usually a two-dimensional grid). The proposed system consists of two modified versions of the functional of the well-known Fuzzy c-means clustering algorithm, where the cluster centers or code vectors are distributed on a low dimensional regular grid, for which a penalization term is added with the object of assuring a smooth distribution of the values of the code vectors on said grid. In one of the two cases, the faithfulness to the data is achieved by minimizing the differences between the data and the code vectors, and in the other case, the new functional is based on the probability density estimation of the input data. <IMAGE>
AU2001218647A 2000-12-12 2000-12-12 Non-linear data mapping and dimensionality reduction system Abandoned AU2001218647A1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/ES2000/000466 WO2002048962A1 (en) 2000-12-12 2000-12-12 Non-linear data mapping and dimensionality reduction system

Publications (1)

Publication Number Publication Date
AU2001218647A1 true AU2001218647A1 (en) 2002-06-24

Family

ID=8244291

Family Applications (1)

Application Number Title Priority Date Filing Date
AU2001218647A Abandoned AU2001218647A1 (en) 2000-12-12 2000-12-12 Non-linear data mapping and dimensionality reduction system

Country Status (6)

Country Link
US (1) US20040078351A1 (en)
EP (1) EP1353295B1 (en)
AT (1) ATE371229T1 (en)
AU (1) AU2001218647A1 (en)
DE (1) DE60036138T2 (en)
WO (1) WO2002048962A1 (en)

Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1494127A1 (en) * 2003-07-01 2005-01-05 Semeion Method, computer program and computer readable means for projecting data from a multidimensional space into a space having less dimensions and to carry out a cognitive analysis on said data.
US7974977B2 (en) * 2007-05-03 2011-07-05 Microsoft Corporation Spectral clustering using sequential matrix compression
WO2010023334A1 (en) * 2008-08-29 2010-03-04 Universidad Politécnica de Madrid Method for reducing the dimensionality of data
US8332337B2 (en) * 2008-10-17 2012-12-11 Lockheed Martin Corporation Condition-based monitoring system for machinery and associated methods
US8948513B2 (en) * 2009-01-27 2015-02-03 Apple Inc. Blurring based content recognizer
CN102053992B (en) * 2009-11-10 2014-12-10 阿里巴巴集团控股有限公司 Clustering method and system
US8523075B2 (en) 2010-09-30 2013-09-03 Apple Inc. Barcode recognition using data-driven classifier
US8905314B2 (en) 2010-09-30 2014-12-09 Apple Inc. Barcode recognition using data-driven classifier
US9015093B1 (en) * 2010-10-26 2015-04-21 Michael Lamport Commons Intelligent control with hierarchical stacked neural networks
KR101242509B1 (en) 2011-02-17 2013-03-18 경북대학교 산학협력단 Design method of activation function for inference of fuzzy cognitive maps in mobile program and thereof system
CN102289664B (en) * 2011-07-29 2013-05-08 北京航空航天大学 Method for learning non-linear face movement manifold based on statistical shape theory
US20160259857A1 (en) * 2015-03-06 2016-09-08 Microsoft Technology Licensing, Llc User recommendation using a multi-view deep learning framework
CN105956611B (en) * 2016-04-25 2019-05-21 西安电子科技大学 Based on the SAR image target recognition method for identifying non-linear dictionary learning
CN108073978A (en) * 2016-11-14 2018-05-25 顾泽苍 A kind of constructive method of the ultra-deep learning model of artificial intelligence
CN109995884B (en) * 2017-12-29 2021-01-26 北京京东尚科信息技术有限公司 Method and apparatus for determining precise geographic location

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5016188A (en) * 1989-10-02 1991-05-14 Rockwell International Corporation Discrete-time optimal control by neural network
US5930781A (en) * 1992-10-27 1999-07-27 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Neural network training by integration of adjoint systems of equations forward in time
US6038337A (en) * 1996-03-29 2000-03-14 Nec Research Institute, Inc. Method and apparatus for object recognition
FR2754080B1 (en) * 1996-10-01 1998-10-30 Commissariat Energie Atomique LEARNING METHOD FOR THE CLASSIFICATION OF DATA ACCORDING TO TWO CLASSES SEPARATED BY A SEPARATING SURFACE OF ORDER 1 OR 2

Also Published As

Publication number Publication date
ATE371229T1 (en) 2007-09-15
WO2002048962A1 (en) 2002-06-20
US20040078351A1 (en) 2004-04-22
EP1353295A1 (en) 2003-10-15
EP1353295B1 (en) 2007-08-22
DE60036138T2 (en) 2008-05-21
DE60036138D1 (en) 2007-10-04

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