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Found 19 papers, 2 papers with code
Local Background Estimation for Improved Gas Plume Identification in Hyperspectral Images
Deep learning identification models have shown promise for identifying gas plumes in Longwave IR hyperspectral images of urban scenes, particularly when a large library of gases are being considered.
Manifold Alignment with Label Information
With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains.
Diffusion Transport Alignment
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
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 tuning.
GPS-Denied Navigation Using SAR Images and Neural Networks
This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system.
Extendable and invertible manifold learning with geometry regularized autoencoders
Our regularization, based on the diffusion potential distances from the recently-proposed PHATE visualization method, encourages the learned latent representation to follow intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and reconstruction of data in the original feature space from latent coordinates.
Supervised Visualization for Data Exploration
We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structures in data, while emphasizing important variables in the low-dimensional embedding.
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Here, we consider multiple levels of abstraction via a multiresolution geometry of data points at different granularities.
Visualizing High Dimensional Dynamical Processes
Manifold learning techniques for dynamical systems and time series have shown their utility for a broad spectrum of applications in recent years.
Convergence Rates for Empirical Estimation of Binary Classification Bounds
We derive a bound on the convergence rate for the Friedman-Rafsky (FR) estimator of the HP-divergence, which is related to a multivariate runs statistic for testing between two distributions.
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
DyMoN is well-suited to the idiosyncrasies of biological data, including noise, sparsity, and the lack of longitudinal measurements in many types of systems.
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
Considering the $k$-nearest neighbor ($k$-NN) graph of $Y$ in the joint data set $(X, Y)$, we show that the average powered ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN points is proportional to R\'{e}nyi divergence of $X$ and $Y$ densities.
Information Theoretic Structure Learning with Confidence
Information theoretic measures (e. g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension.
The intrinsic value of HFO features as a biomarker of epileptic activity
High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity.
Meta learning of bounds on the Bayes classifier error
For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier.
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the $R$ value.
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis
We find the relationship between complexity of an active region as measured by Mount Wilson and the intrinsic dimension of its image patches.
Multivariate f-Divergence Estimation With Confidence
The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics.
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images.
