Dimensionality Reduction (DR) is useful to understand high-dimensional data. It attracts
wide attention from industry and academia and is employed in areas such as machine …

We propose a novel supervised dimension-reduction method called supervised t-distributed
stochastic neighbor embedding (St-SNE) that achieves dimension reduction by preserving …

t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality
reduction and visualization that has become widely popular in recent years. Efficient …

The dimensionality reduction has been widely introduced to use the high-dimensional data
for regression, classification, feature analysis, and visualization. As the one technique of …

T-distributed stochastic neighbor embedding (tSNE) is a popular and prize-winning
approach for dimensionality reduction and visualizing high-dimensional data. However …

Data visualization is a powerful tool and widely adopted by organizations for its
effectiveness to abstract the right information, understand, and interpret results clearly and …

Dimensionality reduction and manifold learning methods such as t-distributed stochastic
neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two …

The growth of smart city applications is increasingly around the world, many cities invest in
the development of these systems intending to improve the management and life of their …

Nonparametric dimensionality reduction (DR) techniques such as locally linear embedding
or t-distributed stochastic neighbor (t-SNE) embedding constitute standard tools to visualize …

Stochastic Neighbor Embedding (SNE) and variants are dimensionality reduction (DR)
methods able to foil the curse of dimensionality to deliver outstanding experimental results …