Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold ; day:24 ; month:2 ; year:2023 ; pages:1-36
Information geometry ; (24.2.2023), 1-36
- Urheber
-
Ghalamkari, Kazu
Sugiyama, Mahito
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s41884-023-00100-5
- URN
-
urn:nbn:de:101:1-2023063021281467339138
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:55 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Ghalamkari, Kazu
- Sugiyama, Mahito
- SpringerLink (Online service)
Ähnliche Objekte (12)
Low-rank approximations of nonseparable panel models
Low-rank approximations of nonseparable panel models
Low‐rank approximations of frequency‐limited discrete‐time Gramians
Topics in learning sparse and low-rank models of non-negative data
Dynamical low‐rank approximations of solutions to the Hamilton–Jacobi–Bellman equation
A new method based on the manifold-alternative approximating for low-rank matrix completion
A Low-Rank Matrix Approach to Compute Polynomial Approximations of Smooth Two-Dimensional Functions
Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis
Computing f-Divergences and Distances of High-Dimensional Probability Density Functions — Low-Rank Tensor Approximations —
Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data
Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations
Low-rank Parareal: a low-rank parallel-in-time integrator
Low-rank approximations of nonseparable panel models
Low-rank approximations of nonseparable panel models
Low‐rank approximations of frequency‐limited discrete‐time Gramians
Topics in learning sparse and low-rank models of non-negative data
Dynamical low‐rank approximations of solutions to the Hamilton–Jacobi–Bellman equation
A new method based on the manifold-alternative approximating for low-rank matrix completion
A Low-Rank Matrix Approach to Compute Polynomial Approximations of Smooth Two-Dimensional Functions
Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis
Computing f-Divergences and Distances of High-Dimensional Probability Density Functions — Low-Rank Tensor Approximations —
Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data
Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations
Low-rank Parareal: a low-rank parallel-in-time integrator
Low-rank approximations of nonseparable panel models
Low-rank approximations of nonseparable panel models
Low‐rank approximations of frequency‐limited discrete‐time Gramians
Topics in learning sparse and low-rank models of non-negative data
Dynamical low‐rank approximations of solutions to the Hamilton–Jacobi–Bellman equation
A new method based on the manifold-alternative approximating for low-rank matrix completion
A Low-Rank Matrix Approach to Compute Polynomial Approximations of Smooth Two-Dimensional Functions
Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis
Computing f-Divergences and Distances of High-Dimensional Probability Density Functions — Low-Rank Tensor Approximations —
Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data
Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations