M. Kirby

发表

Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

James N. Knight, Charles W. Anderson, David A. Peterson, 2005, EURASIP J. Adv. Signal Process..

Covariance Regularization for Supervised Learning in High Dimensions

Michael Kirby, Charles W. Anderson, Daniel L. Elliott, 2010 .

EEG Subspace Representations and Feature Selection for Brain-Computer Interfaces

Charles W. Anderson, Michael J. Kirby, C. Anderson, 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

Geometric Analysis for the Characterization of Nonstationary Time Series

Michael Kirby, Charles W. Anderson, M. Kirby, 2003 .

Set-to-Set Face Recognition Under Variations in Pose and Illumination

M. Kirby, Jen-Mei Chang, C. Peterson, 2007, 2007 Biometrics Symposium.

Feature Patch Illumination Spaces and Karcher Compression for Face Recognition via Grassmannians

M. Kirby, Jen-Mei Chang, C. Peterson, 2012 .

Manifold Curvature Descriptors from Hypersurface Integral Invariants

M. Kirby, C. Peterson, Javier Álvarez-Vizoso, 2018, 1804.04808.

Geometry of curves in Rn from the local singular value decomposition

B. Draper, M. Kirby, C. Peterson, 2019, Linear Algebra and its Applications.

Integral Invariants from Covariance Analysis of Embedded Riemannian Manifolds

M. Kirby, C. Peterson, Javier Álvarez-Vizoso, 2018, 1804.10425.

Manifold curvature learning from hypersurface integral invariants

M. Kirby, C. Peterson, Javier Álvarez-Vizoso, 2020 .

Local eigenvalue decomposition for embedded Riemannian manifolds

M. Kirby, C. Peterson, Javier Álvarez-Vizoso, 2020 .

Geometric subspace methods and time-delay embedding for EEG artifact removal and classification

C.W. Anderson, J.N. Knight, T. O'Connor, 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Dimensionality Reduction Using Secant-Based Projection Methods: The Induced Dynamics in Projected Systems

D. Broomhead, M. Kirby, 2005 .

A Fractal Dimension for Measures via Persistent Homology

Patrick D. Shipman, M. Kirby, C. Peterson, 2018, 1808.01079.

Reconstructing phase space from PDE simulations

Dieter Armbruster, Michael Kirby, D. Armbruster, 1992 .

Kernel/Feature Selection for Support Vector Machines Applied to Materials Design

Michael Kirby, Rick Miranda, M. Kirby, 2000 .

Geometry of Curves in R from the Local Singular Value Decomposition

M. Kirby, C. Peterson, Robert T. Arn, 2019 .

THE MAX-LENGTH-VECTOR LINE OF BEST FIT TO A COLLECTION OF VECTOR SUBSPACES

Brent R. Davis, M. Kirby, C. Peterson, 2013 .

Illumination Spaces in YDB and CMU-PIE

J. Beveridge, B. Draper, M. Kirby, 2009 .

A time‐dependent Lagrangian eyewall

G. Dangelmayr, M. Kirby, B. Rutherford, 2012 .

THESIS NON-LINEAR PRINCIPAL COMPONENT ANALYSIS AND CLASSIFICATION OF EEG DURING MENTAL TASKS Submitted by

M. Kirby, S. Devulapalli, 2016 .

Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection

M. Kirby, T. Ghosh, 2022, ArXiv.

Estimating chemical concentrations from compressed hyperspectral images

M. Kirby, C. Peterson, Stephanie M. Craig, 2022, Defense + Commercial Sensing.

Self-organizing mappings on the flag manifold with applications to hyper-spectral image data analysis

M. Kirby, C. Peterson, Xiaofeng Ma, 2021, Neural Computing and Applications.

Geometry of Curves in $\mathbb R^n$, Singular Value Decomposition, and Hankel Determinants

B. Draper, M. Kirby, C. Peterson, 2015, 1511.05008.

Supervised Dimensionality Reduction and Visualization using Centroid-encoder pdf

Michael Kirby, Tomojit Ghosh, M. Kirby, 2020, J. Mach. Learn. Res..

Persistence Images: A Stable Vector Representation of Persistent Homology

Henry Adams, Tegan Emerson, Michael Kirby, 2015, J. Mach. Learn. Res..

Modeling Multivariate Time Series on Manifolds with Skew Radial Basis Functions

Arta A. Jamshidi, Michael J. Kirby, M. Kirby, 2011, Neural Computation.

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Michael Kirby, Chris Peterson, Julia R. Dupuis, 2019, Defense + Commercial Sensing.

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

Michael Kirby, Chris Peterson, Julia R. Dupuis, 2019, Computational Imaging V.

Explorations in Very Early Prognosis of the Human Immune Response to Influenza

Xiaofeng Ma, Xiaoyu Liu, Michael Kirby, 2016, BCB.

Dantzig-Selector Radial Basis Function Learning with Nonconvex Refinement

Xiaofeng Ma, Michael Kirby, Tomojit Ghosh, 2016 .

Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks

M. Kirby, C. Peterson, Nathaniel Blanchard, 2022, 2022 IEEE International Conference on Big Data (Big Data).

Longwave infrared compressive hyperspectral imager

Julia Rentz Dupuis, Michael Kirby, B. R. Cosofret, 2015, Commercial + Scientific Sensing and Imaging.

ENDMEMBER EXTRACTION ON THE GRASSMANNIAN

Michael Kirby, Chris Peterson, Henry Kvinge, 2018, 2018 IEEE Data Science Workshop (DSW).

The Flag Median and FlagIRLS

M. Kirby, C. Peterson, E. King, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Circular Nodes in Neural Networks

R. Miranda, M. J. Kirby, M. Kirby, 1996, Neural Computation.

Action classification on product manifolds

J. Ross Beveridge, Yui Man Lui, Michael Kirby, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

On Diffusively Coupled Oscillators

G. Dangelmayr, M. Kirby, 1992 .

Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets

Michael Kirby, Chris Peterson, Henry Kvinge, 2018, 2018 IEEE International Conference on Big Data (Big Data).

A data-driven approach to sampling matrix selection for compressive sensing pdf

Christian W. Smith, Michael Kirby, Chris Peterson, 2019, Computational Imaging V.

Exploring Musical Structure Using Tonnetz Lattice Geometry and LSTMs

Xiaofeng Ma, Michael Kirby, Manuchehr Aminian, 2020, ICCS.

A flag representation for finite collections of subspaces of mixed dimensions

Bruce A. Draper, Michael Kirby, Chris Peterson, 2014 .

A sequential simplex algorithm for automatic data and center selecting radial basis functions

Xiaofeng Ma, Michael Kirby, Tomojit Ghosh, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Estimation of Topological Dimension

Michael J. Kirby, Douglas R. Hundley, M. Kirby, 2003, SDM.

New tools for the visualization of biological pathways.

Michael Kirby, Xiaofeng Ma, Tomojit Ghosh, 2018, Methods.

Band selection in hyperspectral imagery using sparse support vector machines

Christopher M. Gittins, Michael Kirby, Sofya Chepushtanova, 2014, Defense + Security Symposium.

Classification of hyperspectral imagery on embedded Grassmannians

Michael Kirby, Sofya Chepushtanova, M. Kirby, 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns

Michael Kirby, M. Kirby, 2000 .

Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder

M. Kirby, T. Ghosh, 2023, ArXiv.

A New Approach to Dimensionality Reduction: Theory and Algorithms

David S. Broomhead, Michael J. Kirby, D. Broomhead, 2000, SIAM J. Appl. Math..

Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder

M. Kirby, T. Ghosh, 2023, ArXiv.

Manifold Curvature From Covariance Analysis

Michael Kirby, Chris Peterson, Javier Alvarez-Vizoso, 2018, 2018 IEEE Statistical Signal Processing Workshop (SSP).

Subspace Quantization on the Grassmannian

Michael Kirby, Chris Peterson, Shannon Stiverson, 2019, WSOM+.

An order fitting rule for optimal subspace averaging

Louis L. Scharf, Michael Kirby, Chris Peterson, 2016, 2016 IEEE Statistical Signal Processing Workshop (SSP).

The max‐length‐vector line of best fit to a set of vector subspaces and an optimization problem over a set of hyperellipsoids

Michael Kirby, Chris Peterson, Daniel J. Bates, 2015, Numer. Linear Algebra Appl..

Finding the Subspace Mean or Median to Fit Your Need

Bruce A. Draper, J. Ross Beveridge, Michael Kirby, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data

Michael Kirby, Chris Peterson, Henry Kvinge, 2019, WSOM+.

Geometric Manifold Learning

David S. Broomhead, Arta A. Jamshidi, Michael J. Kirby, 2011, IEEE Signal Processing Magazine.

The Whitney Reduction Network: A Method for Computing Autoassociative Graphs

David S. Broomhead, Michael J. Kirby, D. Broomhead, 2001, Neural Computation.

Skew-Radial Basis Function Expansions for Empirical Modeling

Arta A. Jamshidi, Michael J. Kirby, M. Kirby, 2009, SIAM J. Sci. Comput..

Sparse Grassmannian Embeddings for Hyperspectral Data Representation and Classification

Michael Kirby, Sofya Chepushtanova, M. Kirby, 2017, IEEE Geoscience and Remote Sensing Letters.

Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection

M. Kirby, Yajing Liu, Nathaniel Blanchard, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Illumination Face Spaces Are Idiosyncratic

Bruce A. Draper, J. Ross Beveridge, Michael Kirby, 2006, IPCV.

The Bilipschitz criterion for dimension reduction mapping design

Michael J. Kirby, Douglas R. Hundley, Markus Anderle, 2002, Intell. Data Anal..