Technology development has accelerated the volume growth of complex data, such as
images, videos, time series, and georeferenced data. Similarity search is a widely used …

We propose a novel approach to solving the approximate k-nearest neighbor search
problem in metric spaces. The search structure is based on a navigable small world graph …

The recent introduction of 3D shape analysis frameworks able to quantify the deformation of
a shape into another in terms of the variation of real functions yields a new interpretation of …

Time-series classification has been addressed by a plethora of machine-learning
techniques, including neural networks, support vector machines, Bayesian approaches, and …

We present a new similarity search library and discuss a variety of design and performance
issues related to its development. We adopt a position that engineering is equally important …

In data science and content-based retrieval, we find many domain-specific techniques that
employ a data processing pipeline with two fundamental steps. First, data entities are …

We introduce a new problem of retrieving 3D models that are deformable to a given query
shape and present a novel deep deformation-aware embedding to solve this retrieval task …

Many well-known machine learning algorithms have been applied to the task of time-series
classification, including decision trees, neural networks, support vector machines and …

As research interest surges, vector similarity search is applied in multiple fields, including
data mining, computer vision, and information retrieval.{Given a set of objects (eg, a set of …

Similarity search based on a distance function in metric spaces is a fundamental problem for
many applications. Queries for similar objects lead to the well-known machine learning task …