UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied
Riemannian geometry and algebraic topology to find low-dimensional embeddings of …

A classical problem that frequently compromises Recommender System (RS) accuracy is
the sparsity of the data about the interactions of the users with the items to be recommended …

This work outlines a unified formulation to represent spectral approaches for both
dimensionality reduction and clustering. Proposed formulation starts with a generic latent …

Dimensionality reduction (DR) aims to reveal salient properties of high-dimensional (HD)
data in a low-dimensional (LD) representation space. Two elements stipulate success of a …

This work introduces a generalized kernel perspective for spectral dimensionality reduction
approaches. Firstly, an elegant matrix view of kernel principal component analysis (PCA) is …

Nowadays, a consequence of data overload is that world's technology capacity to collect,
communicate, and store large volumes of data is increasing faster than human analysis …

Dimensionality reduction (DR) approaches are often a crucial step in data analysis tasks,
particularly for data visualization purposes. DR-based techniques are essentially designed …

Dimensionality reduction aims to preserve, as much as possible, the significant structure of
high-dimensional data in the low-dimensional space. This allows removing noise and …

This work describes a new model for interactive data visualization followed from a
dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting …

Dimensionality reduction (DR) methods are able to produce low-dimensional
representations of an input data sets which may become intelligible for human perception …