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The Half Space Proximal (HSP) graph is a low out-degree monotonic graph with wide applications in various domains, including combinatorial optimization in strings, enhancing kNN classification, simplifying chemical networks, estimating local ...

Dynamic Time Warping (DTW) is a well-known similarity measure between time series data. Although DTW can calculate the similarity between time series with different lengths, it is computationally expensive. Therefore, fast algorithms that ...

In this paper, we delve into the Mutual k-Nearest Neighbor Graph ($m$k$\mathit{NNG}$) and its significance in clustering and outlier detection. We present a rigorous mathematical framework elucidating its application and highlight its role in the success of ...$$$\mathit{}$

Searching for similar objects in intrinsically high-dimensional data sets is a challenging task. Sketches have been proposed for faster similarity search using linear scans. Binary sketches are one such approach to find a good mapping from the ...

Computing a similarity measure (or a distance) between two complex objects is a fundamental building block for a huge number of applications in a wide variety of domains. Since many tasks involve computing such similarities among many pairs of ...

An arduous biomedical task involves condensing evidence derived from multiple interrelated studies, given a context as input, to generate reviews or provide answers autonomously. We named this task context-aware multi-document summarization (CA-...

The number of Android apps is constantly on the rise. Existing stores allow selecting apps from general named categories. To prevent miscategorization and facilitate user selection of the appropriate app, a closer examination of the categories’ ...

A side-sharing tandem is a rectangular array that is composed of two adjacent non-overlapping occurrences of the same rectangular block. Furthering our understanding of side-sharing tandems should facilitate the development of more efficient 2d ...$$$$${}^{\mathrm{}}$

This paper introduces Turbo Scan (TS), a novel k-nearest neighbor search solution tailored for high-dimensional data and specific workloads where indexing can’t be efficiently amortized over time. There exist situations where the overhead of index ...

The nearest prototype classification is a less computationally intensive replacement for the $k$-NN method, especially when large datasets are considered. Centroids are often used as prototypes to represent whole classes in metric spaces. Selection ...

Most papers on similarity retrieval present experiments executed on an assortion of complex datasets. However, no work focuses on analyzing the selection of datasets to evaluate the techniques proposed in the related literature. Ideally, the ...

User relevance feedback (URF) is emerging as an important component of the multimedia analytics toolbox. State-of-the-art URF systems employ high-dimensional vectors of semantic features and train linear-SVM classifiers in each round of ...

The k-means clustering algorithm is a popular algorithm that partitions data into k clusters. There are many improvements to accelerate the standard algorithm. Most current research employs upper and lower bounds on point-to-cluster distances and ...

Traditional evaluation of an approximate high-dimensional index typically consists of running a benchmark with known ground truth, analyzing the performance in terms of traditional result quality and latency measures, and then comparing those ...

The seminal work of Broder et al. [5] introduces the $\text{minHash}$ algorithm that computes a low-dimensional sketch of high-dimensional binary data that closely approximates pairwise Jaccard similarity. Since its invention, $\text{minHash}$ has been commonly ...$$$$$$$$

Sparse Data Observers (SDO) is an unsupervised learning approach developed to cover the need for fast, highly interpretable and intuitively parameterizable anomaly detection. We present SDOclust, an extension that performs clustering while ...

We investigate the k-closest pair problem in high dimensions, that is finding the $k\ge 1$ closest pairs of points in a set $S\subseteq \mathcal{X}$ in a metric space $(\mathcal{X},\text{dist})$. This is a fundamental problem in computational geometry with a wide variety of applications, ...

The rapid development of deep learning and artificial intelligence has transformed our approach to solving scientific problems across various domains, including computer vision, natural language processing, and automatic content generation. ...

Within the topic of similarity search, all work we know assumes that search is based on a dissimilarity space, where a query comprises a single object in the space.

Here, we examine the possibility of a multiple-object query. There are at least ...

The Similarity Join (SJ) has become one of the most popular and valuable data processing operators in analyzing large amounts of data. Various types of similarity join operators have been effectively used in multiple scenarios. However, these ...

This manuscript presents the premiere SISAP 2023 Indexing Challenge, which seeks replicable and competitive solutions in the realm of approximate similarity search algorithms. Our aim is recall, all while optimizing build time, search time, and ...

Locality-Sensitive-Hashing (LSH) plays a crucial role in approximate nearest neighbour search and similarity-based queries. In this paper, we present a study on the performance of LSH for indexing and searching high-dimensional binary vectors ...

Despite the efficacy of graph-based algorithms for Approximate Nearest Neighbor (ANN) searches, the optimal tuning of such systems remains unclear. This study introduces a method to tune the performance of off-the-shelf graph-based indexes, ...

This submission into the SISAP Indexing Challenge examines the experimental setup and performance of the Learned Metric Index, which uses an architecture of interconnected learned models to answer similarity queries. An inherent part of this ...

The Hierarchical Navigable Small World (HNSW) Graph is a graph-based approximate similarity search algorithm that achieves fast and accurate search through a hierarchical structure providing long-range and short-range links. The HNSW remains as a ...

Recent advances in cross-modal multimedia data analysis necessarily require efficient similarity search on the scales of hundreds of millions of high-dimensional vectors. We address this task by proposing the CRANBERRY algorithm that specifically ...