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Indexing Multiple-Instance Objects

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Database and Expert Systems Applications (DEXA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10439))

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Abstract

As an actively investigated topic in machine learning, Multiple-Instance Learning (MIL) has many proposed solutions, including supervised and unsupervised methods. We introduce an indexing technique supporting efficient queries on Multiple-Instance (MI) objects. Our technique has a dynamic structure that supports efficient insertions and deletions and is based on an effective similarity measure for MI objects. Some MIL approaches have proposed their similarity measures for MI objects, but they either do not use all information or are time consuming. In this paper, we use two joint Gaussian based measures for MIL, Joint Gaussian Similarity (JGS) and Joint Gaussian Distance (JGD). They are based on intuitive definitions and take all the information into account while being robust to noise. For JGS, we propose the Instance based Index for querying MI objects. For JGD, metric trees can be directly used as the index because of its metric properties. Extensive experimental evaluations on various synthetic and real-world data sets demonstrate the effectiveness and efficiency of the similarity measures and the performance of the corresponding index structures.

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Notes

  1. 1.

    https://drive.google.com/open?id=0B3LRCuPdnX1BMFViblpaS1VKZmM.

  2. 2.

    https://drive.google.com/open?id=0B3LRCuPdnX1BVHFjeWpiLWF3M2M.

  3. 3.

    https://archive.ics.uci.edu/ml/machine-learning-databases/musk/.

  4. 4.

    http://www.miproblems.org/datasets/foxtigerelephant/.

  5. 5.

    https://drive.google.com/open?id=0B3LRCuPdnX1BYXpGUzlxYVdsSDA.

  6. 6.

    The time cost in this paper refers to the CPU time.

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Author information

Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Linfei Zhou, Wei Ye & Christian Böhm

  2. University of Electronic Science and Technology of China, Chengdu, China

    Zhen Wang

  3. University of Vienna, Vienna, Austria

    Claudia Plant

Authors
  1. Linfei Zhou
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  2. Wei Ye
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  3. Zhen Wang
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  4. Claudia Plant
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  5. Christian Böhm
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Corresponding author

Correspondence to Christian Böhm .

Editor information

Editors and Affiliations

  1. University of Lyon, Villeurbanne, France

    Djamal Benslimane

  2. University of Milan, Milan, Italy

    Ernesto Damiani

  3. University of Michigan, Dearborn, Michigan, USA

    William I. Grosky

  4. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  5. Wright State University, Dayton, Ohio, USA

    Amit Sheth

  6. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Zhou, L., Ye, W., Wang, Z., Plant, C., Böhm, C. (2017). Indexing Multiple-Instance Objects. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-64471-4_13

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  • Online ISBN: 978-3-319-64471-4

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