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Titlebook: Similarity Search and Applications; 16th International C Oscar Pedreira,Vladimir Estivill-Castro Conference proceedings 2023 The Editor(s)

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Mutual ,-Nearest Neighbor Graph for Data Analysis: Application to Metric Space Clustering mathematical framework elucidating its application and highlight its role in the success of various clustering algorithms. Building on Brito et al.’s findings, which link the connected components of the .k. to clusters under specific density bounds, we explore its relevance in the context of a wide
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Unbiased Similarity Estimators Using Samples variety of domains. Since many tasks involve computing such similarities among many pairs of objects, many algorithmic techniques and data structures have been devised in the past to reduce the number of similarity computations and to reduce the complexity of computing the similarity (e.g., dimensi
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Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensionsxing can’t be efficiently amortized over time. There exist situations where the overhead of index construction isn’t warranted given the few queries executed on the dataset..Rooted in the Johnson-Lindenstrauss (JL) lemma, our approach sidesteps the need for random rotations. To validate TS’s superio
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Class Representatives Selection in Non-metric Spaces for Nearest Prototype Classificationd. Centroids are often used as prototypes to represent whole classes in metric spaces. Selection of class prototypes in non-metric spaces is more challenging as the idea of computing centroids is not directly applicable. Instead, a set of representative objects can be used as the class prototype..In
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Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedbacknsional vectors of semantic features and train linear-SVM classifiers in each round of interaction. In a round, they present the user with the most confident media items, which lie furthest from the SVM plane. Due to the scale of current media collections, URF systems must be supported by a high-dim
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