Informatics, transborder data flows and law - the new challenges. - Michael Kirby (Comput. Law Secur. Rev., 1989)

Information security - OECD initiatives. - Michael Kirby (Comput. Law Secur. Rev., 1992)

A model problem in the representation of digital image sequences. - Michael Kirby; F. Weisser; Gerhard Dangelmayr (Pattern Recognit., 1993)

Biotechnology and the law. - Michael Kirby (Comput. Law Secur. Rev., 1994)

Adaptive Antenna Beam Forming Via Maximum Noise Fraction for Multi Carrier CDMA Systems. - Fatemeh Emdad; Seyed A. Zekavat; Michael Kirby (International Conference on Wireless Networks, 2003)

Illumination Face Spaces Are Idiosyncratic. - Jen-Mei Chang; J. Ross Beveridge; Bruce A. Draper; Michael Kirby; Holger Kley; Chris Peterson 001 (IPCV, 2006)

Recognition of Digital Images of the Human Face at Ultra Low Resolution Via Illumination Spaces. - Jen-Mei Chang; Michael Kirby; Holger Kley; Chris Peterson 001; Bruce A. Draper; J. Ross Beveridge (ACCV (2), 2007)

Feature Extraction via Kernelized Signal Fraction Analysis vs Kernelized Principal Component Analysis. - Fatemeh Emdad; Michael Kirby; Seyed A. Zekavat (DMIN, 2008)

Image-set matching using a geodesic distance and cohort normalization. - Yui Man Lui; J. Ross Beveridge; Bruce A. Draper; Michael Kirby (FG, 2008)

Principal Angles Separate Subject Illumination Spaces in YDB and CMU-PIE. - J. Ross Beveridge; Bruce A. Draper; Jen-Mei Chang; Michael Kirby; Holger Kley; Chris Peterson 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Action classification on product manifolds. - Yui Man Lui; J. Ross Beveridge; Michael Kirby (CVPR, 2010)

Accurate fault prediction of BlueGene/P RAS logs via geometric reduction. - Joshua Thompson; David W. Dreisigmeyer; Terry R. Jones; Michael Kirby; Joshua Ladd (DSN Workshops, 2010)

Locally Linear Embedding Clustering Algorithm for Natural Imagery - Lori Ziegelmeier; Michael Kirby; Chris Peterson 001 (CoRR, 2012)

Flag Manifolds for the Characterization of Geometric Structure in Large Data Sets. - Tim Marrinan; J. Ross Beveridge; Bruce A. Draper; Michael Kirby; Chris Peterson 001 (ENUMATH, 2013)

Finding the Subspace Mean or Median to Fit Your Need. - Tim Marrinan; Bruce A. Draper; J. Ross Beveridge; Michael Kirby; Chris Peterson 001 (CVPR, 2014)

Classification of hyperspectral imagery on embedded Grassmannians. - Sofya Chepushtanova; Michael Kirby (WHISPERS, 2014)

An application of persistent homology on Grassmann manifolds for the detection of signals in hyperspectral imagery. - Sofya Chepushtanova; Michael Kirby; Chris Peterson 001; Lori Ziegelmeier (IGARSS, 2015)

Fourier-ring descriptor to characterize rare circulating cells from images generated using immunofluorescence microscopy. - Tegan Emerson; Michael Kirby; Kelly Bethel; Anand Kolatkar; Madelyn Luttgen; Stephen O'Hara; Paul K. Newton; Peter Kuhn (Comput. Medical Imaging Graph., 2015)

Persistence Images: An Alternative Persistent Homology Representation. - Sofya Chepushtanova; Tegan Emerson; Eric M. Hanson; Michael Kirby; Francis C. Motta; Rachel Neville; Chris Peterson 001; Patrick D. Shipman; Lori Ziegelmeier (CoRR, 2015)

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems). - Salman Habib 002; Robert Roser; Tom LeCompte; Zach Marshall; Anders Borgland; Brett Viren; Peter Nugent; Makoto Asai; Lothar Bauerdick; Hal Finkel; Steven Gottlieb; Stefan Hoeche; et al. (CoRR, 2015)

The max-length-vector line of best fit to a set of vector subspaces and an optimization problem over a set of hyperellipsoids. - Daniel J. Bates; Brent R. Davis; Michael Kirby; Justin Marks; Chris Peterson 001 (Numer. Linear Algebra Appl., 2015)

Explorations in Very Early Prognosis of the Human Immune Response to Influenza. - Mmanu Chaturvedi; Tomojit Ghosh; Michael Kirby; Xiaoyu Liu 003; Xiaofeng Ma; Shannon Stiverson (BCB, 2016)

Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies. - Sofya Chepushtanova; Michael Kirby; Chris Peterson 001; Lori Ziegelmeier (CTIC, 2016)

An order fitting rule for optimal subspace averaging. - Ignacio Santamaría; Louis L. Scharf; Chris Peterson 001; Michael Kirby; Joseph M. Francos (SSP, 2016)

Reduced dimension estimators in matched subspace detection. - Tegan Emerson; Michael Kirby; Chris Peterson 001; Louis Scharf (WHISPERS, 2016)

Constrained subspace estimation via convex optimization. - Ignacio Santamaría; Javier Vía; Michael Kirby; Tim Marrinan; Chris Peterson 001; Louis Scharf (EUSIPCO, 2017)

Sparse Locally Linear Embedding. - Lori Ziegelmeier; Michael Kirby; Chris Peterson 001 (ICCS, 2017)

A sequential simplex algorithm for automatic data and center selecting radial basis functions. - Xiaofeng Ma; Tomojit Ghosh; Michael Kirby (IJCNN, 2017)

Visualizing data sets on the Grassmannian using self-organizing mappings. - Michael Kirby; Chris Peterson 001 (WSOM, 2017)

Persistence Images: A Stable Vector Representation of Persistent Homology. - Henry Adams; Tegan Emerson; Michael Kirby; Rachel Neville; Chris Peterson 001; Patrick D. Shipman; Sofya Chepushtanova; Eric M. Hanson; Francis C. Motta; Lori Ziegelmeier (J. Mach. Learn. Res., 2017)

Sparse Grassmannian Embeddings for Hyperspectral Data Representation and Classification. - Sofya Chepushtanova; Michael Kirby (IEEE Geosci. Remote. Sens. Lett., 2017)

Stratifying High-Dimensional Data Based on Proximity to the Convex Hull Boundary. - Lori Ziegelmeier; Michael Kirby; Chris Peterson 001 (SIAM Rev., 2017)

Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (IEEE BigData, 2018)

Endmember Extraction on the Grassmannian. - Elin Farnell; Henry Kvinge; Michael Kirby; Chris Peterson 001 (DSW, 2018)

Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (HPEC, 2018)

A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (ISPDC, 2018)

Manifold Curvature From Covariance Analysis. - Javier Álvarez-Vizoso; Michael Kirby; Chris Peterson 001 (SSP, 2018)

Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (CoRR, 2018)

A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data. - Henry Kvinge; Michael Kirby; Chris Peterson 001; Chad Eitel; Tod Clapp (WSOM+, 2019)

Self-Organizing Mappings on the Flag Manifold. - Xiaofeng Ma; Michael Kirby; Chris Peterson 001 (WSOM+, 2019)

Subspace Quantization on the Grassmannian. - Shannon Stiverson; Michael Kirby; Chris Peterson 001 (WSOM+, 2019)

A data-driven approach to sampling matrix selection for compressive sensing. - Elin Farnell; Henry Kvinge; John P. Dixon; Julia R. Dupuis; Michael Kirby; Chris Peterson 001; Elizabeth C. Schundler; Christian W. Smith (CoRR, 2019)

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing. - Henry Kvinge; Elin Farnell; Julia R. Dupuis; Michael Kirby; Chris Peterson 001; Elizabeth C. Schundler (CoRR, 2019)

Error-adaptive modeling of streaming time-series data using radial basis functions. - Xiaofeng Ma; Manuchehr Aminian; Michael Kirby (J. Comput. Appl. Math., 2019)

Motion Segmentation via Generalized Curvatures. - Robert T. Arn; Pradyumna Narayana; Tegan Emerson; Bruce A. Draper; Michael Kirby; Chris Peterson 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Exploring Musical Structure Using Tonnetz Lattice Geometry and LSTMs. - Manuchehr Aminian; Eric Kehoe; Xiaofeng Ma; Amy Peterson; Michael Kirby (ICCS (2), 2020)

The flag manifold as a tool for analyzing and comparing data sets. - Xiaofeng Ma; Michael Kirby; Chris Peterson 001 (CoRR, 2020)

Self-organizing mappings on the Grassmannian with applications to data analysis in high dimensions. - Xiaofeng Ma; Michael Kirby; Chris Peterson 001; Louis Scharf (Neural Comput. Appl., 2020)

The Flag Manifold as a Tool for Analyzing and Comparing Sets of Data Sets. - Xiaofeng Ma; Michael Kirby; Chris Peterson 001 (ICCVW, 2021)

Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks. - Huma Jamil; Yajing Liu; Christina M. Cole; Nathaniel Blanchard; Emily J. King; Michael Kirby; Christopher Peterson 001 (IEEE Big Data, 2022)

The Flag Median and FlagIRLS. - Nathan Mankovich; Emily J. King; Chris Peterson 001; Michael Kirby (CVPR, 2022)

Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection. - Tomojit Ghosh; Michael Kirby (CoRR, 2022)

Towards an HPC Complementary Computing Facility. - Kenneth Herner; Michael Kirby; Steven Timm (CoRR, 2022)

Supervised Dimensionality Reduction and Visualization using Centroid-Encoder. - Tomojit Ghosh; Michael Kirby (J. Mach. Learn. Res., 2022)

Self-organizing mappings on the flag manifold with applications to hyper-spectral image data analysis. - Xiaofeng Ma; Michael Kirby; Chris Peterson 001 (Neural Comput. Appl., 2022)

Sparse Linear Centroid-Encoder: A Biomarker Selection tool for High Dimensional Biological Data. - Tomojit Ghosh; Karim Karimov; Michael Kirby (BIBM, 2023)

Feature Selection on Big Data using Masked Sparse Bottleneck Centroid-Encoder. - Tomojit Ghosh; Michael Kirby (IEEE Big Data, 2023)

Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection. - Huma Jamil; Yajing Liu; Turgay Caglar; Christina M. Cole; Nathaniel Blanchard; Christopher Peterson 001; Michael Kirby (CVPR Workshops, 2023)

ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homology. - Yajing Liu; Christina M. Cole; Chris Peterson 001; Michael Kirby (TAG-ML, 2023)

Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder. - Tomojit Ghosh; Michael Kirby (CoRR, 2023)

Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder. - Tomojit Ghosh; Michael Kirby (CoRR, 2023)

Sparse Linear Centroid-Encoder: A Convex Method for Feature Selection. - Tomojit Ghosh; Michael Kirby; Karim Karimov (CoRR, 2023)

ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog. - Yajing Liu; Christina M. Cole; Chris Peterson 001; Michael Kirby (CoRR, 2023)

Leveraging linear mapping for model-agnostic adversarial defense. - Huma Jamil; Yajing Liu; Nathaniel Blanchard; Michael Kirby; Chris Peterson 001 (Frontiers Comput. Sci., 2023)

Exploring fMRI RDMs: enhancing model robustness through neurobiological data. - William Pickard; Kelsey Sikes; Huma Jamil; Nicholas Chaffee; Nathaniel Blanchard; Michael Kirby; Chris Peterson 001 (Frontiers Comput. Sci., 2023)

An algorithm for computing Schubert varieties of best fit with applications. - Karim Karimov; Michael Kirby; Chris Peterson 001 (Frontiers Artif. Intell., 2023)

Locally linear attributes of ReLU neural networks. - Benjamin Sattelberg; Renzo Cavalieri; Michael Kirby; Chris Peterson 001; J. Ross Beveridge (Frontiers Artif. Intell., 2023)

Nonlinear feature selection using sparsity-promoted centroid-encoder. - Tomojit Ghosh; Michael Kirby (Neural Comput. Appl., 2023)

A Multi-domain Multi-task Approach for Feature Selection from Bulk RNA Datasets. - Karim Salta; Tomojit Ghosh; Michael Kirby (ICCS (4), 2024)

Optimizing Sparse Generalized Singular Vectors for Feature Selection in Proximal Support Vector Machines with Application to Breast and Ovarian Cancer Detection. - Ugochukwu O. Ugwu; Michael Kirby (CoRR, 2024)

Fine-tuning foundational models to code diagnoses from veterinary health records. - Mayla R. Boguslav; Adam Kiehl; David Kott; G. Joseph Strecker; Tracy Webb; Nadia Saklou; Terri Ward; Michael Kirby (CoRR, 2024)

Integrating geometries of ReLU feedforward neural networks. - Yajing Liu; Turgay Caglar; Chris Peterson 001; Michael Kirby (Frontiers Big Data, 2024)

Linear Centroid Encoder for Supervised Principal Component Analysis. - Tomojit Ghosh; Michael Kirby (Pattern Recognit., 2024)

Correction to: Nonlinear feature selection using sparsity-promoted centroid-encoder. - Tomojit Ghosh; Michael Kirby (Neural Comput. Appl., 2024)

Towards Sustainable AI: Evaluating Efficiency of LLMs for Coding of Diagnoses from Veterinary Health Records. - David Kott; Adam Kiehl; Connor Price; Tracy L. Webb; Nadia Saklou; G. Joseph Strecker; Michael Kirby (SDS, 2025)