  Dimensionality Reduction (DR) techniques can generate 2D projections and
enable visual exploration of cluster structures of high-dimensional datasets.
However, different DR techniques would yield various patterns, which
significantly affect the performance of visual cluster analysis tasks. We
present the results of a user study that investigates the influence of
different DR techniques on visual cluster analysis. Our study focuses on the
most concerned property types, namely the linearity and locality, and evaluates
twelve representative DR techniques that cover the concerned properties. Four
controlled experiments were conducted to evaluate how the DR techniques
facilitate the tasks of 1) cluster identification, 2) membership
identification, 3) distance comparison, and 4) density comparison,
respectively. We also evaluated users' subjective preference of the DR
techniques regarding the quality of projected clusters. The results show that:
1) Non-linear and Local techniques are preferred in cluster identification and
membership identification; 2) Linear techniques perform better than non-linear
techniques in density comparison; 3) UMAP (Uniform Manifold Approximation and
Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the
best in cluster identification and membership identification; 4) NMF
(Nonnegative Matrix Factorization) has competitive performance in distance
comparison; 5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has
competitive performance in density comparison.
