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    TopSpin: TOPic Discovery via Sparse Principal Component INterference

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    Type
    Book Chapter
    Authors
    Takáč, Martin
    Ahipaşaoğlu, Selin Damla
    Cheung, Ngai-Man
    Richtarik, Peter cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-02-14
    Online Publication Date
    2019-02-15
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/631654
    
    Metadata
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    Abstract
    We propose a novel topic discovery algorithm for unlabeled images based on the bag-of-words (BoW) framework. We first extract a dictionary of visual words and subsequently for each image compute a visual word occurrence histogram. We view these histograms as rows of a large matrix from which we extract sparse principal components (PCs). Each PC identifies a sparse combination of visual words which co-occur frequently in some images but seldom appear in others. Each sparse PC corresponds to a topic, and images whose interference with the PC is high belong to that topic, revealing the common parts possessed by the images. We propose to solve the associated sparse PCA problems using an Alternating Maximization (AM) method, which we modify for the purpose of efficiently extracting multiple PCs in a deflation scheme. Our approach attacks the maximization problem in SPCA directly and is scalable to high-dimensional data. Experiments on automatic topic discovery and category prediction demonstrate encouraging performance of our approach. Our SPCA solver is publicly available.
    Citation
    Takáč M, Ahipaşaoğlu SD, Cheung N-M, Richtárik P (2019) TopSpin: TOPic Discovery via Sparse Principal Component INterference. Research on Intelligent Manufacturing: 157–180. Available: http://dx.doi.org/10.1007/978-3-030-12119-8_8.
    Sponsors
    This work was partially supported by the U.S. National Science Foundation, under award number NSF:CCF:1618717, NSF:CMMI:1663256 and NSF:CCF:1740796.
    Publisher
    Springer International Publishing
    Journal
    Brain-Inspired Intelligence and Visual Perception
    DOI
    10.1007/978-3-030-12119-8_8
    arXiv
    1311.1406
    Additional Links
    http://link.springer.com/chapter/10.1007/978-3-030-12119-8_8
    http://arxiv.org/pdf/1311.1406
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-12119-8_8
    Scopus Count
    Collections
    Computer Science Program; Book Chapters; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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