Subspace Clustering for Sequential Data

Stephen Tierney, Junbin Gao, Yi Guo

    Research output: Book chapter/Published conference paperConference paperpeer-review

    94 Citations (Scopus)
    5 Downloads (Pure)

    Abstract

    We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as NCut for final segmentation. In contrast our technique, under certain conditions, is capable of segmenting clusters intrinsically without providing the number of clusters as a parameter. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include a new penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral data, video sequences and face images. Our experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.
    Original languageEnglish
    Title of host publicationCVPR 2014
    Place of PublicationUSA
    PublisherIEEE Xplore
    Pages1019-1026
    Number of pages8
    DOIs
    Publication statusPublished - 2014
    EventConference on Computer Vision and Pattern Recognition - Columbus, Ohio
    Duration: 23 Jun 201428 Jun 2014

    Publication series

    Name
    ISSN (Print)1063-6919

    Conference

    ConferenceConference on Computer Vision and Pattern Recognition
    Period23/06/1428/06/14

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