Geometric component analysis and its applications to data analysis
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Publication:2036489
DOI10.1016/j.acha.2021.02.005OpenAlexW3134997426MaRDI QIDQ2036489
Moshe Salhov, Amit Bermanis, Amir Z. Averbuch
Publication date: 29 June 2021
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2021.02.005
diffusion mapsdictionary constructionincomplete pivoted \textit{QR}landmark data pointslinear and non-linear dimensionality reduction
Uses Software
Cites Work
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- CUR matrix decompositions for improved data analysis
- A randomized algorithm for the decomposition of matrices
- Diffusion forecasting model with basis functions from QR-decomposition
- Diffusion representations
- A simple proof of the restricted isometry property for random matrices
- Embedding Riemannian manifolds by their heat kernel
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- Multiscale data sampling and function extension
- The geometry of graphs and some of its algorithmic applications
- Cover-based bounds on the numerical rank of Gaussian kernels
- Greedy algorithms for reduced bases in Banach spaces
- Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension
- Diffusion maps
- Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
- Optimal CUR Matrix Decompositions
- Convergence Rates for Greedy Algorithms in Reduced Basis Methods
- Randomized Algorithms for Matrices and Data
- Clustering for edge-cost minimization (extended abstract)
- Extensions of Lipschitz mappings into a Hilbert space
- Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods
- Tighter bounds for random projections of manifolds
- Relative-Error $CUR$ Matrix Decompositions
- On the Compression of Low Rank Matrices
- Fast monte-carlo algorithms for finding low-rank approximations
- Scattered Data Approximation
