160 citations · 380 across the 6 of their papers we have counts for
6 papers
Can Self-Organizing Maps accurately predict photometric redshifts?
M. J. Way, C. D. Klose
We present an unsupervised machine learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization approach calle…
Viewpoints: A high-performance high-dimensional exploratory data analysis tool
P. R. Gazis, C. Levit, M. J. Way
Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets,…
The Revolution in Astronomy Education: Data Science for the Masses
Kirk D. Borne, Suzanne Jacoby, K. Carney +5
As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys…
Large Scale Structure in the SDSS DR1 Galaxy Survey
A. Doroshkevich, D. L. Tucker, S. Allam +1
The Large Scale Structure in the galaxy distribution is investigated using The First Data Release of the Sloan Digital Sky Survey. Using the Minimal Spanning Tree technique we have…
Non-Gaussianity of the derived maps from the first-year WMAP data
Lung-Yih Chiang, Pavel D. Naselsky, Oleg V. Verkhodanov +1
We present non-Gaussianity testing on recently-released derived maps from the first-year WMAP data by Tegmark, de Oliveria-Costa and Hamilton. Our test is based on a phase mapping…
Large Scale Structure in the SDSS Galaxy Survey
A. Doroshkevich, D. L. Tucker, S. Allam
The Large Scale Structure (LSS) in the galaxy distribution is investigated using the Sloan Digital Sky Survey Early Data Release (SDSS EDR). Using the Minimal Spanning Tree techniq…