3 citations · 4 across the 4 of their papers we have counts for
9 papers
Manifold Alignment with Label Information
Andres F. Duque, Myriam Lizotte, Guy Wolf +1
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used f…
GPS-Denied Navigation Using SAR Images and Neural Networks
Teresa White, Jesse Wheeler, Colton Lindstrom +2
Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method…
Supervised Visualization for Data Exploration
Jake S. Rhodes, Adele Cutler, Guy Wolf +1
Dimensionality reduction is often used as an initial step in data exploration, either as preprocessing for classification or regression or for visualization. Most dimensionality re…
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle +7
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here…
Compressed Diffusion
Scott Gigante, Jay S. Stanley, Ngan Vu +4
Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most…
Convergence Rates for Empirical Estimation of Binary Classification Bounds
Salimeh Yasaei Sekeh, Morteza Noshad, Kevin R. Moon +1
Bounding the best achievable error probability for binary classification problems is relevant to many applications including machine learning, signal processing, and information th…