-
Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing
Dimensionality reduction is an integral part of data visualization. It i...
read it
-
Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
Nonlinear dimensionality reduction methods are a popular tool for data s...
read it
-
High dimensionality: The latest challenge to data analysis
The advent of modern technology, permitting the measurement of thousands...
read it
-
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Dimensionality reduction (DR) is frequently used for analyzing and visua...
read it
-
Tensor-Train Parameterization for Ultra Dimensionality Reduction
Locality preserving projections (LPP) are a classical dimensionality red...
read it
-
Contrastive Multiple Correspondence Analysis (cMCA): Applying the Contrastive Learning Method to Identify Political Subgroups
Ideal point estimation and dimensionality reduction have long been utili...
read it
-
A survey of dimensionality reduction techniques
Experimental life sciences like biology or chemistry have seen in the re...
read it
NCVis: Noise Contrastive Approach for Scalable Visualization
Modern methods for data visualization via dimensionality reduction, such as t-SNE, usually have performance issues that prohibit their application to large amounts of high-dimensional data. In this work, we propose NCVis – a high-performance dimensionality reduction method built on a sound statistical basis of noise contrastive estimation. We show that NCVis outperforms state-of-the-art techniques in terms of speed while preserving the representation quality of other methods. In particular, the proposed approach successfully proceeds a large dataset of more than 1 million news headlines in several minutes and presents the underlying structure in a human-readable way. Moreover, it provides results consistent with classical methods like t-SNE on more straightforward datasets like images of hand-written digits. We believe that the broader usage of such software can significantly simplify the large-scale data analysis and lower the entry barrier to this area.
READ FULL TEXT
Comments
There are no comments yet.