High‐dimensional data sets are a prevalent occurrence in many application domains. This
data is commonly visualized using dimensionality reduction (DR) methods. DR methods …

The cover tree data structure speeds up exact nearest neighbor queries over arbitrary metric
spaces. This paper makes cover trees even faster. In particular, we provide (1) a simpler …

This hands-on guide demonstrates how the flexibility of the command line can help you
become a more efficient and productive data scientist. You'll learn how to combine small, yet …

Dimension reduction techniques are essential for feature selection and feature extraction of
complex high‐dimensional data. These techniques, which construct low‐dimensional …

Recent times have witnessed an exponential increase in multimedia specifically visual
contents. Emotions are considered an essential part for extracting facial features, evaluating …

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be
challenging when distinguishing the underlying high-dimensional data clusters in a 2D …

Dimensionality reduction (DR) methods aim to map high-dimensional datasets to 2D
scatterplots for visual exploration. Such scatterplots are used to reason about the cluster …

Discovering patterns of the complex high-dimensional data is one of the fundamental pillars
of modern data science. Dimension reduction and intrinsic dimension estimation are two …

In multivariate classification problems, 2D visualization methods can be very useful to
understand the data properties whenever they transform the n-dimensional data into a set of …

The cover tree (CT) and its improved version are hierarchical data structures that simplified
navigating nets while maintaining good runtime guarantees. They can perform nearest …