59 citations · 252 across the 21 of their papers we have counts for
24 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…
Diffusion Transport Alignment
Andres F. Duque, Guy Wolf, Kevin R. Moon
The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains.…
Geometry- and Accuracy-Preserving Random Forest Proximities
Jake S. Rhodes, Adele Cutler, Kevin R. Moon
Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tunin…
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…
Extendable and invertible manifold learning with geometry regularized autoencoders
Andrés F. Duque, Sacha Morin, Guy Wolf +1
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data…
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…
Visualizing High Dimensional Dynamical Processes
Andrés F. Duque, Guy Wolf, Kevin R. Moon
Manifold learning techniques for dynamical systems and time series have shown their utility for a broad spectrum of applications in recent years. While these methods are effective…
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…
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
Scott Gigante, David van Dijk, Kevin Moon +3
Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describ…
Ensemble Estimation of Distributional Functionals via -Nearest Neighbors
Kevin R. Moon, Kumar Sricharan, Alfred O. Hero
The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to t…
Direct Ensemble Estimation of Density Functionals
Alan Wisler, Kevin Moon, Visar Berisha
Estimating density functionals of analog sources is an important problem in statistical signal processing and information theory. Traditionally, estimating these quantities require…
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
Morteza Noshad, Kevin R. Moon, Salimeh Yasaei Sekeh +1
We propose a direct estimation method for Rényi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets and , re…
Ensemble Estimation of Generalized Mutual Information with Applications to Genomics
Kevin R. Moon, Kumar Sricharan, Alfred O. Hero
Mutual information is a measure of the dependence between random variables that has been used successfully in myriad applications in many fields. Generalized mutual information mea…
Information Theoretic Structure Learning with Confidence
Kevin R. Moon, Morteza Noshad, Salimeh Yasaei Sekeh +1
Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in hi…
Ensemble Estimation of Information Divergence
Kevin R. Moon, Kumar Sricharan, Kristjan Greenewald +1
Recent work has focused on the problem of nonparametric estimation of information divergence functionals. Many existing approaches are restrictive in their assumptions on the densi…
The intrinsic value of HFO features as a biomarker of epileptic activity
Stephen V. Gliske, Kevin R. Moon, William C. Stacey +1
High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis…
Meta learning of bounds on the Bayes classifier error
Kevin R. Moon, Veronique Delouille, Alfred O. Hero
Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single bas…
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
Kevin R. Moon, Veronique Delouille, Jimmy J. Li +3
Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and Mc…
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis
Kevin R. Moon, Jimmy J. Li, Veronique Delouille +3
The flare-productivity of an active region is observed to be related to its spatial complexity. Mount Wilson or McIntosh sunspot classifications measure such complexity but in a ca…
Multivariate f-Divergence Estimation With Confidence
Kevin R. Moon, Alfred O. Hero
The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics. While several nonparametric divergence estimators exist,…
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Kevin R. Moon, Jimmy J. Li, Veronique Delouille +2
Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images. Their complexity is correlate…
Ensemble estimation of multivariate f-divergence
Kevin R. Moon, Alfred O. Hero
f-divergence estimation is an important problem in the fields of information theory, machine learning, and statistics. While several divergence estimators exist, relatively few of…