# Frank Nielsen

Ontology type: schema:Person

NAME

Frank

SURNAME

Nielsen

### Publications in SciGraph latest 50 shown

• 2019 Monte Carlo Information-Geometric Structures in GEOMETRIC STRUCTURES OF INFORMATION
• 2018-11-20 Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation in NEURAL COMPUTING AND APPLICATIONS
• 2017-10-24 On the Error Exponent of a Random Tensor with Orthonormal Factor Matrices in GEOMETRIC SCIENCE OF INFORMATION
• 2017-10-24 Bregman Divergences from Comparative Convexity in GEOMETRIC SCIENCE OF INFORMATION
• 2017-10-24 k-Means Clustering with Hölder Divergences in GEOMETRIC SCIENCE OF INFORMATION
• 2017 Batch and Online Mixture Learning: A Review with Extensions in COMPUTATIONAL INFORMATION GEOMETRY
• 2017 On Clustering Financial Time Series: A Need for Distances Between Dependent Random Variables in COMPUTATIONAL INFORMATION GEOMETRY
• 2017 Fast $$(1+\epsilon )$$ ( 1 + ϵ ) -Approximation of the Löwner Extremal Matrices of High-Dimensional Symmetric Matrices in COMPUTATIONAL INFORMATION GEOMETRY
• 2017 Erratum to: Computational Information Geometry in COMPUTATIONAL INFORMATION GEOMETRY
• 2016 Quantifying the Invariance and Robustness of Permutation-Based Indexing Schemes in SIMILARITY SEARCH AND APPLICATIONS
• 2016 Patch Matching with Polynomial Exponential Families and Projective Divergences in SIMILARITY SEARCH AND APPLICATIONS
• 2015 Online k-MLE for Mixture Modeling with Exponential Families in GEOMETRIC SCIENCE OF INFORMATION
• 2015 Clustering Random Walk Time Series in GEOMETRIC SCIENCE OF INFORMATION
• 2015 Bag-of-Components: An Online Algorithm for Batch Learning of Mixture Models in GEOMETRIC SCIENCE OF INFORMATION
• 2015 Approximating Covering and Minimum Enclosing Balls in Hyperbolic Geometry in GEOMETRIC SCIENCE OF INFORMATION
• 2014 Hartigan’s Method for $$k$$ k -MLE: Mixture Modeling with Wishart Distributions and Its Application to Motion Retrieval in GEOMETRIC THEORY OF INFORMATION
• 2013 Fast Learning of Gamma Mixture Models with k-MLE in SIMILARITY-BASED PATTERN RECOGNITION
• 2013 Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review in SIMILARITY-BASED PATTERN RECOGNITION
• 2013 A New Implementation of k-MLE for Mixture Modeling of Wishart Distributions in GEOMETRIC SCIENCE OF INFORMATION
• 2013 Mining Matrix Data with Bregman Matrix Divergences for Portfolio Selection in MATRIX INFORMATION GEOMETRY
• 2013 Boosting k-Nearest Neighbors Classification in ADVANCED TOPICS IN COMPUTER VISION
• 2013 Learning Mixtures by Simplifying Kernel Density Estimators in MATRIX INFORMATION GEOMETRY
• 2013 Jensen Divergence-Based Means of SPD Matrices in MATRIX INFORMATION GEOMETRY
• 2013 Hypothesis Testing, Information Divergence and Computational Geometry in GEOMETRIC SCIENCE OF INFORMATION
• 2013 k-NN Boosting Prototype Learning for Object Classification in ANALYSIS, RETRIEVAL AND DELIVERY OF MULTIMEDIA CONTENT
• 2012-12 Boosting k-NN for Categorization of Natural Scenes in INTERNATIONAL JOURNAL OF COMPUTER VISION
• 2012 Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability in MACHINE LEARNING IN MEDICAL IMAGING
• 2012 Boosting Nearest Neighbors for the Efficient Estimation of Posteriors in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
• 2011 Skew Jensen-Bregman Voronoi Diagrams in TRANSACTIONS ON COMPUTATIONAL SCIENCE XIV
• 2011 Multi-class Leveraged κ-NN for Image Classification in COMPUTER VISION – ACCV 2010
• 2011 Video Stippling in ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS
• 2010-09 Bregman Voronoi Diagrams in DISCRETE & COMPUTATIONAL GEOMETRY
• 2010 Texture Regimes for Entropy-Based Multiscale Image Analysis in COMPUTER VISION – ECCV 2010
• 2010 Levels of Details for Gaussian Mixture Models in COMPUTER VISION – ACCV 2009
• 2009 Searching High-Dimensional Neighbours: CPU-Based Tailored Data-Structures Versus GPU-Based Brute-Force Method in COMPUTER VISION/COMPUTER GRAPHICS COLLABORATIONTECHNIQUES
• 2009 Clustering Multivariate Normal Distributions in EMERGING TRENDS IN VISUAL COMPUTING
• 2009 Abstracts of the LIX Fall Colloquium 2008: Emerging Trends in Visual Computing in EMERGING TRENDS IN VISUAL COMPUTING
• 2009 Intrinsic Geometries in Learning in EMERGING TRENDS IN VISUAL COMPUTING
• 2008 Real-Time Spherical Videos from a Fast Rotating Camera in IMAGE ANALYSIS AND RECOGNITION
• 2007 Customized Slider Bars for Adjusting Multi-dimension Parameter Sets in SMART GRAPHICS
• 2006 Copy-Paste Synthesis of 3D Geometry with Repetitive Patterns in SMART GRAPHICS
• 2005-02 Surround video: a multihead camera approach in THE VISUAL COMPUTER
• 2005 Interactive Point-and-Click Segmentation for Object Removal in Digital Images in COMPUTER VISION IN HUMAN-COMPUTER INTERACTION
• 2005 Fitting the Smallest Enclosing Bregman Ball in MACHINE LEARNING: ECML 2005
• 2003 A Sketching Interface for Modeling the Internal Structures of 3D Shapes in SMART GRAPHICS
• 2002-04 HyperMask – projecting a talking head onto a real object in THE VISUAL COMPUTER
• 2002-01-29 Maintenance of a Piercing Set for Intervals with Applications in ALGORITHMS AND COMPUTATION
• 2000 Grouping and Querying: A Paradigm to Get Output-Sensitive Algorithms in DISCRETE AND COMPUTATIONAL GEOMETRY

### Identifiers

JSON-LD is the canonical representation for SciGraph data.

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

[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"affiliation": [
{
"affiliation": {
"id": "https://www.grid.ac/institutes/grid.452725.3",
"type": "Organization"
},
"isCurrent": true,
"type": "OrganizationRole"
},
{
"id": "https://www.grid.ac/institutes/grid.10877.39",
"type": "Organization"
}
],
"familyName": "Nielsen",
"givenName": "Frank",
"id": "sg:person.012062051333.43",
"identifier": {
"name": "orcid_id",
"type": "PropertyValue",
"value": [
"0000-0001-5728-0726"
]
},
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012062051333.43",
"https://orcid.org/0000-0001-5728-0726"
],
"sdDataset": "persons",
"sdDatePublished": "2019-03-07T14:07",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-researchers-20181010/20181011/dim_researchers/base/researchers_1883.json",
"type": "Person"
}
]

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/person.012062051333.43'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/person.012062051333.43'

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/person.012062051333.43'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/person.012062051333.43'