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Capped Lp-Norm Graph Embedding for Photo Clustering

Published: 01 October 2016 Publication History

Abstract

Photos are a predominant source of information on a global scale. Cluster analysis of photos can be applied to situation recognition and understanding cultural dynamics. Graph-based learning provides a current approach for modeling data in clustering problems. However, the performance of this framework depends heavily on initial graph construction by input data. Data outliers degrade graph quality, leading to poor clustering results. We designed a new capped lp-norm graph-based model to reduce the impact of outliers. This is accomplished by allowing the data graph to self adjust as part of the graph embedding. Furthermore, we derive an iterative algorithm to solve the objective function optimization problem. Experiments on four real-world benchmark data sets and Yahoo Flickr Creative Commons data set show the effectiveness of this new graph-based capped lp-norm clustering method.

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    cover image ACM Conferences
    MM '16: Proceedings of the 24th ACM international conference on Multimedia
    October 2016
    1542 pages
    ISBN:9781450336031
    DOI:10.1145/2964284
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    Publication History

    Published: 01 October 2016

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    Author Tags

    1. capped LP-norm
    2. photo clustering
    3. unsupervised learning
    4. yahoo flickr creative commons data

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    • Short-paper

    Funding Sources

    • Donald Bren Chair funds for Prof. Ramesh Jain

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    MM '16
    Sponsor:
    MM '16: ACM Multimedia Conference
    October 15 - 19, 2016
    Amsterdam, The Netherlands

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    MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

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    • (2025)Improved safe semi-supervised clustering based on capped ℓ21 normFuzzy Sets and Systems10.1016/j.fss.2025.109276(109276)Online publication date: Jan-2025
    • (2023)Determination of hyperparameter and similarity norm for electrical tomography algorithm using clustering validity indexMeasurement10.1016/j.measurement.2023.112976216(112976)Online publication date: Jul-2023
    • (2022)Iteratively Reweighted Algorithm for Fuzzy $c$-MeansIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.314882330:10(4310-4321)Online publication date: Oct-2022
    • (2021)Adaptive weighted least squares regression for subspace clusteringKnowledge and Information Systems10.1007/s10115-021-01612-1Online publication date: 11-Oct-2021
    • (2020)Build2VecProceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design10.5555/3465085.3465155(1-4)Online publication date: 25-May-2020
    • (2019)Deep Representation Learning for Social Network AnalysisFrontiers in Big Data10.3389/fdata.2019.000022Online publication date: 3-Apr-2019
    • (2019)Adaptive $L_{p}$ Regularization for Electrical Impedance TomographyIEEE Sensors Journal10.1109/JSEN.2019.294007019:24(12297-12305)Online publication date: 15-Dec-2019
    • (2019)A Novel Fuzzy c-Means Clustering Algorithm Using Adaptive NormInternational Journal of Fuzzy Systems10.1007/s40815-019-00740-9Online publication date: 22-Oct-2019
    • (2018)Robust mst-based clustering algorithmNeural Computation10.1162/neco_a_0108130:6(1624-1646)Online publication date: 1-Jun-2018
    • (2018)A Comprehensive Survey of Graph Embedding: Problems, Techniques, and ApplicationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.280745230:9(1616-1637)Online publication date: 1-Sep-2018
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