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05/30/2023
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Finding meaningful representations and distances of hierarchical data is...
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07/18/2022
ManiFeSt: Manifold-based Feature Selection for Small Data Sets
In this paper, we present a new method for few-sample supervised feature...
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01/21/2022
Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators
Multivariate time-series have become abundant in recent years, as many d...
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10/12/2021
Single Independent Component Recovery and Applications
Latent variable discovery is a central problem in data analysis with a b...
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10/07/2021
Time Series Forecasting Using Manifold Learning
We address a three-tier numerical framework based on manifold learning f...
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04/03/2021
Joint Geometric and Topological Analysis of Hierarchical Datasets
In a world abundant with diverse data arising from complex acquisition t...
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09/17/2020
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing
In this paper, we consider data acquired by multimodal sensors capturing...
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09/09/2020
Kernel-based parameter estimation of dynamical systems with unknown observation functions
A low-dimensional dynamical system is observed in an experiment as a hig...
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07/28/2020
Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation
In this paper, we present new results on the Riemannian geometry of symm...
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06/23/2020
Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels
While several electrocardiogram-derived respiratory (EDR) algorithms hav...
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04/09/2020
Spectral Discovery of Jointly Smooth Features for Multimodal Data
In this paper, we propose a spectral method for deriving functions that ...
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03/12/2020
Option Discovery in the Absence of Rewards with Manifold Analysis
Options have been shown to be an effective tool in reinforcement learnin...
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06/03/2019
Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation
The problem of domain adaptation has become central in many applications...
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04/19/2019
Optimal Recovery of Mahalanobis Distance in High Dimension
In this paper, we study the problem of Mahalanobis distance (MD) estimat...
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06/01/2018
Intrinsic Isometric Manifold Learning with Application to Localization
Data living on manifolds commonly appear in many applications. We show t...
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02/26/2018
Data-Driven Source Separation Based on Simplex Analysis
Blind source separation (BSS) is addressed, using a novel data-driven ap...
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11/16/2017
Parametric Manifold Learning Via Sparse Multidimensional Scaling
We propose a metric-learning framework for computing distance-preserving...
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08/18/2017
Data-Driven Tree Transforms and Metrics
We consider the analysis of high dimensional data given in the form of a...
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08/13/2017
Mahalanonbis Distance Informed by Clustering
A fundamental question in data analysis, machine learning and signal pro...
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01/13/2017
Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment
The problem of information fusion from multiple data-sets acquired by mu...
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11/25/2016
Multimodal Latent Variable Analysis
Consider a set of multiple, multimodal sensors capturing a complex syste...
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06/14/2016
Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery
In this paper, we address the problem of hidden common variables discove...
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04/11/2016
Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection
In this paper, we address the problem of multiple view data fusion in th...
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01/30/2016
Latent common manifold learning with alternating diffusion: analysis and applications
The analysis of data sets arising from multiple sensors has drawn signif...
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11/06/2015
