Skip to Main content Skip to Navigation
Conference papers

A Proposition for Fixing the Dimensionality of a Laplacian Low-rank Approximation of any Binary Data-matrix

Alain Lelu 1, 2 Martine Cadot 3
2 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
3 ABC - Machine Learning and Computational Biology
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
Abstract : Laplacian low-rank approximations are much appreciated in the context of graph spectral methods and Correspondence Analysis. We address here the problem of determining the dimensionality K* of the relevant eigenspace of a general binary datatable by a statistically well-founded method. We propose 1) a general framework for graph adjacency matrices and any rectangular binary matrix, 2) a randomization test for fixing K*. We illustrate with both artificial and real data.
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00773436
Contributor : Martine Cadot <>
Submitted on : Tuesday, March 19, 2013 - 8:34:10 PM
Last modification on : Tuesday, October 27, 2020 - 2:34:29 PM
Long-term archiving on: : Thursday, June 20, 2013 - 4:21:32 PM

File

eKnow013_lelu_cadot_OK2.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00773436, version 1

Citation

Alain Lelu, Martine Cadot. A Proposition for Fixing the Dimensionality of a Laplacian Low-rank Approximation of any Binary Data-matrix. The Fifth International Conference on Information, Process, and Knowledge Management - eKNOW 2013, Feb 2013, Nice, France. pp.70-73. ⟨hal-00773436⟩

Share

Metrics

Record views

855

Files downloads

154