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Data mining / Geostatistics / Machine learning / K-means clustering / Hierarchical clustering / Spectral clustering / Consensus clustering / Determining the number of clusters in a data set / Statistics / Cluster analysis / Data analysis


Discovering Video Clusters from Visual Features and Noisy Tags Arash Vahdat, Guang-Tong Zhou, and Greg Mori School of Computing Science, Simon Fraser University, Canada {avahdat,gza11,mori}@cs.sfu.ca
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Document Date: 2014-06-23 15:27:44

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File Size: 2,24 MB

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YouTube / Vahdat / /

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Facility

To bridge / Simon Fraser University / /

IndustryTerm

recent solution / plane algorithm / clustering algorithm / descent algorithm / vision algorithms / web videos / unconstrained web video analysis / unconstrained web videos / coordinate descent-style algorithm / search results / /

Organization

Greg Mori School of Computing Science / U.S. Securities and Exchange Commission / Simon Fraser University / /

Person

Greg Mori Structured / Greg Mori Binary Tags / Greg Mori / Arash Vahdat / Max-Margin Clustering / Guang-Tong Zhou / /

Position

noisy tag model for this clustering / representative / /

ProgrammingLanguage

L / K / /

Technology

K-means algorithm / alternating descent algorithm / cutting plane algorithm / training algorithm / clustering algorithm / same K-means algorithm / vision algorithms / flash / coordinate descent-style algorithm / PDF / /

URL

www.pdffactory.com / /

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