ELMVIS: A nonlinear visualization technique using random permutations and ELMs

Anton Akusok, Amaury Lendasse, Francesco Corona, Rui Nian, Yoan Miche

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

1 Citeringar (Scopus)

Sammanfattning

The extreme learning machine (ELM)-based visualization method (ELMVIS) is proposed Their exact position is weakly relevant to data and can be chosen arbitrarily as a grid or Gaussian distributed points. The prototypes are then randomly assigned to data points, and an ELM is used to estimate the reconstruction error. To train the visualizer, several points are chosen, their assignment permuted, and the error re-estimated. Although the exact solution requires a factorial number of trials experiments show acceptable convergence rates with up to several hundred points due to the ELM's extremely fast reconstruction error estimation. ELMVIS starts by initializing N visualization space points, taken either from a Gaussian distribution or from a regular grid. Then an ELM is initialized, and the ordering matrix is set to an identity matrix. An initial reconstruction MSE is calculated, after which an iteration starts by choosing a random number of samples.

OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftIEEE Intelligent Systems
Volym28
Utgåva6
Sidor (från-till)41-46
Antal sidor6
ISSN1541-1672
StatusPublicerad - 01.01.2013
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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