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Similarity Search and Applications

16th International Conference, SISAP 2023, A Coruña, Spain, October 9–11, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Oscar Pedreira

    1. University of A Coruña, Coruña, Spain

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    You can also search for this editor in PubMed   Google Scholar

  2. Vladimir Estivill-Castro

    1. Pompeu Fabra University, Barcelona, Spain

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14289)

Included in the following conference series:

Conference proceedings info: SISAP 2023.

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Table of contents (26 papers)

  1. Front Matter

    Pages i-xxi
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  2. Research Track

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Finding HSP Neighbors via an Exact, Hierarchical Approach

      • Cole Foster, Edgar Chávez, Benjamin Kimia
      Pages 3-18

    3. Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash

      • Ryota Tomoda, Hisashi Koga
      Pages 19-32

    4. Mutual k-Nearest Neighbor Graph for Data Analysis: Application to Metric Space Clustering

      • Edgar Chavez, Stephane Marchand-Maillet, Adolfo J. Quiroz
      Pages 33-40

    5. An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search

      • Erik Thordsen, Erich Schubert
      Pages 41-55

    6. Unbiased Similarity Estimators Using Samples

      • Conrado Martínez, Alfredo Viola, Jun Wang
      Pages 56-63

    7. Retrieve-and-Rank End-to-End Summarization of Biomedical Studies

      • Gianluca Moro, Luca Ragazzi, Lorenzo Valgimigli, Lorenzo Molfetta
      Pages 64-78

    8. Fine-Grained Categorization of Mobile Applications Through Semantic Similarity Techniques for Apps Classification

      • Elena Flondor, Marc Frincu
      Pages 79-87

    9. Runs of Side-Sharing Tandems in Rectangular Arrays

      • Shoshana Marcus, Dina Sokol, Sarah Zelikovitz
      Pages 88-102

    10. Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensions

      • Edgar Chavez, Eric S. Tellez
      Pages 103-110

    11. Class Representatives Selection in Non-metric Spaces for Nearest Prototype Classification

      • Jaroslav Hlaváč, Martin Kopp, Jan Kohout, Tomá Skopal
      Pages 111-124

    12. The Dataset-Similarity-Based Approach to Select Datasets for Evaluation in Similarity Retrieval

      • Matheus A. L. Matiazzo, Vitor de Castro-Silva, Rafael S. Oyamada, Daniel S. Kaster
      Pages 125-132

    13. Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback

      • Omar Shahbaz Khan, Martin Aumüller, Björn Þór Jónsson
      Pages 133-147

    14. Accelerating k-Means Clustering with Cover Trees

      • Andreas Lang, Erich Schubert
      Pages 148-162

    15. Is Quantized ANN Search Cursed? Case Study of Quantifying Search and Index Quality

      • Gylfi Þór Guðmundsson, Björn Þór Jónsson
      Pages 163-170

    16. Minwise-Independent Permutations with Insertion and Deletion of Features

      • Rameshwar Pratap, Raghav Kulkarni
      Pages 171-184

    17. SDOclust: Clustering with Sparse Data Observers

      • Félix Iglesias, Tanja Zseby, Alexander Hartl, Arthur Zimek
      Pages 185-199

    18. Solving k-Closest Pairs in High-Dimensional Data

      • Martin Aumüller, Matteo Ceccarello
      Pages 200-214

    19. Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval

      • Fabio Carrara, Claudio Gennaro, Lucia Vadicamo, Giuseppe Amato
      Pages 215-222
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  1. Similarity Search and Applications

Keywords

About this book

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9–11, 2023.


The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

Editors and Affiliations

  • University of A Coruña, Coruña, Spain

    Oscar Pedreira

  • Pompeu Fabra University, Barcelona, Spain

    Vladimir Estivill-Castro

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