Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
Medicine Buffalo

Scalable Non-linear Dimensionality Reduction Methods to Accelerate Scientific Discovery

The progress in science and engineering increasingly depends on our ability to analyze massive amounts of observed and simulated data. The vast majority of this data, coming from high-performance high ...
EurekAlert!

A manifold fitting approach for high-dimensional data reduction beyond Euclidean space

Figure 1: Illustration of fitting the latent manifold using the Cycle Generative Adversarial Network (CycleGAN). CycleGAN is a deep learning technique for unsupervised image-to-image translation. In ...
Deep Learning with Yacine on MSN

Visualizing high-dimensional data using PCA in Scikit-Learn

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...
EurekAlert!

Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry

Temperature and heat exchange are at the base of biological processes throughout the realm of Nature. Several of these biological processes are associated with temperature changes in the order of few ...
Nanowerk

A manifold fitting approach for high-dimensional data reduction beyond Euclidean space

(Nanowerk News) National University of Singapore (NUS) statisticians have introduced a new technique that accurately describes high-dimensional data using lower-dimensional smooth structures. This ...