Classification Scheme for Binary Data with Extensions

DOI10.1007/978-3-319-73074-5_4zbMath1443.62186OpenAlexW2968236041MaRDI QIDQ3296178

Deanna Needell, Palina Salanevich, Aaron A. Nelson, Rayan Saab, Denali Molitor

Publication date: 7 July 2020

Published in: Applied and Numerical Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-73074-5_4

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of big data and data science (62R07)

Uses Software

• MNIST
• ImageNet
• AlexNet

Cites Work

• A survey of hierarchical classification across different application domains
• Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
• Fast binary embeddings with Gaussian circulant matrices: improved bounds
• Dimension reduction by random hyperplane tessellations
• One-Bit Compressed Sensing by Linear Programming
• Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
• One-Bit Compressive Sensing With Norm Estimation
• Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
• Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach
• New and Improved Johnson–Lindenstrauss Embeddings via the Restricted Isometry Property
• Extensions of Lipschitz mappings into a Hilbert space
• Support Vector Machines
• On sparse reconstruction from Fourier and Gaussian measurements
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements
• Robust 1-bit Compressive Sensing Using Adaptive Outlier Pursuit
• Simple Classification using Binary Data
• A Review of Hierarchical Classification
• An elementary proof of a theorem of Johnson and Lindenstrauss
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

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