Large-scale semi-supervised learning with online spectral graph sparsification

Daniele Calandriello 1 Alessandro Lazaric 1 Michal Valko 1
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : We introduce Sparse-HFS, a scalable algorithm that can compute solutions to SSL problems using only O(n polylog(n)) space and O(m polylog(n)) time.
Document type :
Conference papers
Complete list of metadatas

Cited literature [10 references]  Display  Hide  Download
https://hal.inria.fr/hal-01544929
Contributor : Michal Valko <>
Submitted on : Thursday, June 22, 2017 - 11:56:25 AM
Last modification on : Friday, March 22, 2019 - 1:34:18 AM
Long-term archiving on : Wednesday, January 10, 2018 - 2:35:29 PM

File

calandriello2015large-scale.pd...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01544929, version 1

Collections

INRIA | CRISTAL-SEQUEL | CRISTAL | UNIV-LILLE

Citation

Daniele Calandriello, Alessandro Lazaric, Michal Valko. Large-scale semi-supervised learning with online spectral graph sparsification. Resource-Efficient Machine Learning workshop at International Conference on Machine Learning, Jul 2015, Lille, France. ⟨hal-01544929⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

247

Files downloads

112

Contact


archive-ouverte@inria.fr