Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2008-2012

FUNDING AMOUNT

115983 USD

ABSTRACT

Collaborative Research: Matrix-Model Machine Learning: unifying machine learning and scientific computing To analyze the ever-growing massive quantities of data for pattern recognition and knowledge discovery, effective machine learning models and efficient computational algorithms are essential tools. The goal of this research is to establish a theoretical foundation for solve challenging machine learning problems utilizing matrix/tensor computational methodologies, leveraging over the success of scientific computing over recent decades - including well-developed algorithms and mature, freely-available software. This research begins with a critical connection between machine learning and scientific computing: an effective global solution to K-means clustering algorithm is provided by the principal component analysis which is based on singular value decomposition (SVD). This fundamental relationship will be systematically extended to matrices, tensors and multi-relational data, to deal with increasingly higher dimensions, multiple indexes and data types. The key goal of this research is to establish that well-known scientific computing techniques such as SVD, matrix and tensor decompositions can be directly utilized for pattern discovery, and further develop these computational methodologies for semi-supervised learning, clustering and classification. The focus will be on multi-index data (tensors, such as a sequence of weather maps or a sequence of traffics over a network) and multi-relational data (multiple pairwise relations, such as protein domains ? proteins ? pathways or words ? documents ? authors). Applications in genomics, text mining, and computer vision will be investigated. More... »

URL

http://www.nsf.gov/awardsearch/showAward?AWD_ID=0830780&HistoricalAwards=false

Related SciGraph Publications

  • 2016-08. Structured sparse CCA for brain imaging genetics via graph OSCAR in BMC SYSTEMS BIOLOGY
  • 2014-03. Emotion Detection via Discriminant Laplacian Embedding in UNIVERSAL ACCESS IN THE INFORMATION SOCIETY
  • 2013-08. Toward structural sparsity: an explicit approach in KNOWLEDGE AND INFORMATION SYSTEMS
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "amount": {
          "currency": "USD", 
          "type": "MonetaryAmount", 
          "value": "115983"
        }, 
        "description": "Collaborative Research: Matrix-Model Machine Learning: unifying machine learning and scientific computing To analyze the ever-growing massive quantities of data for pattern recognition and knowledge discovery, effective machine learning models and efficient computational algorithms are essential tools. The goal of this research is to establish a theoretical foundation for solve challenging machine learning problems utilizing matrix/tensor computational methodologies, leveraging over the success of scientific computing over recent decades - including well-developed algorithms and mature, freely-available software. This research begins with a critical connection between machine learning and scientific computing: an effective global solution to K-means clustering algorithm is provided by the principal component analysis which is based on singular value decomposition (SVD). This fundamental relationship will be systematically extended to matrices, tensors and multi-relational data, to deal with increasingly higher dimensions, multiple indexes and data types. The key goal of this research is to establish that well-known scientific computing techniques such as SVD, matrix and tensor decompositions can be directly utilized for pattern discovery, and further develop these computational methodologies for semi-supervised learning, clustering and classification. The focus will be on multi-index data (tensors, such as a sequence of weather maps or a sequence of traffics over a network) and multi-relational data (multiple pairwise relations, such as protein domains ? proteins ? pathways or words ? documents ? authors). Applications in genomics, text mining, and computer vision will be investigated.", 
        "endDate": "2012-09-30T00:00:00Z", 
        "funder": {
          "id": "https://www.grid.ac/institutes/grid.457785.c", 
          "type": "Organization"
        }, 
        "id": "sg:grant.3092737", 
        "identifier": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "3092737"
            ]
          }, 
          {
            "name": "nsf_id", 
            "type": "PropertyValue", 
            "value": [
              "0830780"
            ]
          }
        ], 
        "inLanguage": [
          "en"
        ], 
        "keywords": [
          "essential tool", 
          "multi-index data", 
          "pathway", 
          "semi", 
          "available software", 
          "classification", 
          "protein", 
          "tensor decomposition", 
          "genomics", 
          "multi-relational data", 
          "sequence", 
          "text mining", 
          "pattern recognition", 
          "fundamental relationship", 
          "goal", 
          "traffic", 
          "machine", 
          "protein domains", 
          "document", 
          "multiple indices", 
          "words", 
          "authors", 
          "application", 
          "principal component analysis", 
          "higher dimensions", 
          "multiple pairwise relations", 
          "mature", 
          "computer vision", 
          "effective machine", 
          "research", 
          "machine learning", 
          "recent decades", 
          "scientific computing techniques", 
          "matrix", 
          "tensor", 
          "efficient computational algorithm", 
          "Matrix-Model Machine Learning", 
          "theoretical foundation", 
          "solve", 
          "computational methodology", 
          "weather maps", 
          "data", 
          "success", 
          "knowledge discovery", 
          "clustering algorithm", 
          "Unifying Machine Learning", 
          "network", 
          "critical connections", 
          "algorithms", 
          "effective global solution", 
          "focus", 
          "data types", 
          "pattern discovery", 
          "problem", 
          "massive quantities", 
          "key goal", 
          "singular value decomposition", 
          "collaborative research", 
          "clustering", 
          "scientific computing", 
          "model", 
          "matrix/tensor", 
          "learning"
        ], 
        "name": "Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing", 
        "recipient": [
          {
            "id": "https://www.grid.ac/institutes/grid.267315.4", 
            "type": "Organization"
          }, 
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.267315.4", 
              "name": "University of Texas at Arlington", 
              "type": "Organization"
            }, 
            "familyName": "Ding", 
            "givenName": "Chris", 
            "id": "sg:person.0615605634.73", 
            "type": "Person"
          }, 
          {
            "member": "sg:person.0615605634.73", 
            "roleName": "PI", 
            "type": "Role"
          }, 
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.267315.4", 
              "name": "University of Texas at Arlington", 
              "type": "Organization"
            }, 
            "familyName": "Huang", 
            "givenName": "Heng", 
            "id": "sg:person.01244671203.95", 
            "type": "Person"
          }, 
          {
            "member": "sg:person.01244671203.95", 
            "roleName": "Co-PI", 
            "type": "Role"
          }
        ], 
        "sameAs": [
          "https://app.dimensions.ai/details/grant/grant.3092737"
        ], 
        "sdDataset": "grants", 
        "sdDatePublished": "2019-03-07T12:33", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/nsf_projects_3.xml.gz", 
        "startDate": "2008-10-01T00:00:00Z", 
        "type": "MonetaryGrant", 
        "url": "http://www.nsf.gov/awardsearch/showAward?AWD_ID=0830780&HistoricalAwards=false"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/grant.3092737'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.3092737'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.3092737'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.3092737'


     

    This table displays all metadata directly associated to this object as RDF triples.

    116 TRIPLES      19 PREDICATES      87 URIs      78 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:grant.3092737 schema:about anzsrc-for:2208
    2 schema:amount Nc29c530f9e364d0eb761e305869450d7
    3 schema:description Collaborative Research: Matrix-Model Machine Learning: unifying machine learning and scientific computing To analyze the ever-growing massive quantities of data for pattern recognition and knowledge discovery, effective machine learning models and efficient computational algorithms are essential tools. The goal of this research is to establish a theoretical foundation for solve challenging machine learning problems utilizing matrix/tensor computational methodologies, leveraging over the success of scientific computing over recent decades - including well-developed algorithms and mature, freely-available software. This research begins with a critical connection between machine learning and scientific computing: an effective global solution to K-means clustering algorithm is provided by the principal component analysis which is based on singular value decomposition (SVD). This fundamental relationship will be systematically extended to matrices, tensors and multi-relational data, to deal with increasingly higher dimensions, multiple indexes and data types. The key goal of this research is to establish that well-known scientific computing techniques such as SVD, matrix and tensor decompositions can be directly utilized for pattern discovery, and further develop these computational methodologies for semi-supervised learning, clustering and classification. The focus will be on multi-index data (tensors, such as a sequence of weather maps or a sequence of traffics over a network) and multi-relational data (multiple pairwise relations, such as protein domains ? proteins ? pathways or words ? documents ? authors). Applications in genomics, text mining, and computer vision will be investigated.
    4 schema:endDate 2012-09-30T00:00:00Z
    5 schema:funder https://www.grid.ac/institutes/grid.457785.c
    6 schema:identifier N25c08574be0541f0b07dd2173bc680f9
    7 Ne116133662154d2bb9ab36ce11db460d
    8 schema:inLanguage en
    9 schema:keywords Matrix-Model Machine Learning
    10 Unifying Machine Learning
    11 algorithms
    12 application
    13 authors
    14 available software
    15 classification
    16 clustering
    17 clustering algorithm
    18 collaborative research
    19 computational methodology
    20 computer vision
    21 critical connections
    22 data
    23 data types
    24 document
    25 effective global solution
    26 effective machine
    27 efficient computational algorithm
    28 essential tool
    29 focus
    30 fundamental relationship
    31 genomics
    32 goal
    33 higher dimensions
    34 key goal
    35 knowledge discovery
    36 learning
    37 machine
    38 machine learning
    39 massive quantities
    40 matrix
    41 matrix/tensor
    42 mature
    43 model
    44 multi-index data
    45 multi-relational data
    46 multiple indices
    47 multiple pairwise relations
    48 network
    49 pathway
    50 pattern discovery
    51 pattern recognition
    52 principal component analysis
    53 problem
    54 protein
    55 protein domains
    56 recent decades
    57 research
    58 scientific computing
    59 scientific computing techniques
    60 semi
    61 sequence
    62 singular value decomposition
    63 solve
    64 success
    65 tensor
    66 tensor decomposition
    67 text mining
    68 theoretical foundation
    69 traffic
    70 weather maps
    71 words
    72 schema:name Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing
    73 schema:recipient N67bce7819f9d40c1842b462632218653
    74 Nb75a1aec963b4e2ea77b24c33e909922
    75 sg:person.01244671203.95
    76 sg:person.0615605634.73
    77 https://www.grid.ac/institutes/grid.267315.4
    78 schema:sameAs https://app.dimensions.ai/details/grant/grant.3092737
    79 schema:sdDatePublished 2019-03-07T12:33
    80 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    81 schema:sdPublisher N4e3590132cda47c4af131cf48f3af178
    82 schema:startDate 2008-10-01T00:00:00Z
    83 schema:url http://www.nsf.gov/awardsearch/showAward?AWD_ID=0830780&HistoricalAwards=false
    84 sgo:license sg:explorer/license/
    85 sgo:sdDataset grants
    86 rdf:type schema:MonetaryGrant
    87 N25c08574be0541f0b07dd2173bc680f9 schema:name dimensions_id
    88 schema:value 3092737
    89 rdf:type schema:PropertyValue
    90 N4e3590132cda47c4af131cf48f3af178 schema:name Springer Nature - SN SciGraph project
    91 rdf:type schema:Organization
    92 N67bce7819f9d40c1842b462632218653 schema:member sg:person.01244671203.95
    93 schema:roleName Co-PI
    94 rdf:type schema:Role
    95 Nb75a1aec963b4e2ea77b24c33e909922 schema:member sg:person.0615605634.73
    96 schema:roleName PI
    97 rdf:type schema:Role
    98 Nc29c530f9e364d0eb761e305869450d7 schema:currency USD
    99 schema:value 115983
    100 rdf:type schema:MonetaryAmount
    101 Ne116133662154d2bb9ab36ce11db460d schema:name nsf_id
    102 schema:value 0830780
    103 rdf:type schema:PropertyValue
    104 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
    105 rdf:type schema:DefinedTerm
    106 sg:person.01244671203.95 schema:affiliation https://www.grid.ac/institutes/grid.267315.4
    107 schema:familyName Huang
    108 schema:givenName Heng
    109 rdf:type schema:Person
    110 sg:person.0615605634.73 schema:affiliation https://www.grid.ac/institutes/grid.267315.4
    111 schema:familyName Ding
    112 schema:givenName Chris
    113 rdf:type schema:Person
    114 https://www.grid.ac/institutes/grid.267315.4 schema:name University of Texas at Arlington
    115 rdf:type schema:Organization
    116 https://www.grid.ac/institutes/grid.457785.c schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...