Chávez / Kimia / Lokoc Similarity Search and Applications

17th International Conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, Proceedings
Erscheinungsjahr 2024
ISBN: 978-3-031-75823-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 15268, 302 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the 17th International Conference on Similarity Search and Applications, SISAP 2024, held in Providence, RI, USA, during November 4–6, 2024.The 13 full papers, 7 short papers and 4 Indexing Challenge papers included in this book were carefully reviewed and selected from 32 submissions. They focus on efficient similarity search methods addressing the challenges of exploring similar items and managing vast machine-learning data sets efficiently.

Zielgruppe

Research

Autoren/Hrsg.

Chávez, Edgar

Kimia, Benjamin

Lokoc, Jakub

Patella, Marco

Sedmidubsky, Jan

Inhaltsverzeichnis

.- Research Track.

.- An Efficient Framework for Approximate Nearest Neighbor Search on High-dimensional Multi-metric Data.

.- REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods.

.- ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods.

.- Demonstrating the Efficacy of Polyadic Queries.

.- Scalable Polyadic Queries.

.- A Dynamic Evaluation Metric for Feature Selection.

.- Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos.

.- Impact of the Neighborhood Parameter on Outlier Detection Algorithms.

.- Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment.

.- Bayesian Estimation Approaches for Local Intrinsic Dimensionality.

.- Towards Personalized Similarity Search for Vector Databases.

.- Information Dissimilarity Measures in Decentralized Knowledge Distillation: A Comparative Analysis.

.- An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search.

.- On the Design of Scalable Outlier Detection Methods using Approximate Nearest Neighbor Graphs.

.- A Topological Evaluation Model for Manifold Learning and Embedding Techniques.

.- Local Intrinsic Dimensionality and the Convergence Order of Fixed-Point Iteration.

.- Identifying Propagating Signals with Spatio-Temporal Clustering in Multivariate Time Series.

.- Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers.

.- Advancing the PAM Algorithm to Semi-Supervised k-Medoids Clustering.

.- Hierarchical Clustering without Pairwise Distances by Incremental Similarity Search.

.- Indexing Challenge.

.- Overview of the SISAP 2024 Indexing Challenge.

.- Scaling Learned Metric Index to 100M Datasets.

.- Grouping Sketches to Index High-Dimensional Data in a Resource Limited Setting.

.- Adapting the Exploration Graph for high throughput in low recall regimes.

.- Top-Down Construction of Locally Monotonic Graphs for Similarity Search.