Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDM.2008.53
Title: Learning by propagability
Authors: Ni, B.
Yan, S. 
Kassim, A. 
Cheong, L.F. 
Issue Date: 2008
Source: Ni, B., Yan, S., Kassim, A., Cheong, L.F. (2008). Learning by propagability. Proceedings - IEEE International Conference on Data Mining, ICDM : 492-501. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDM.2008.53
Abstract: In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and optimal feature representation can constitute a harmonic system, namely, the data labels are invariant with respect to the propagation on the similarity-graph constructed by the optimal feature representation. Based on this philosophy, a unified formulation for learning by propagability is proposed for both supervised and semisupervised configurations. Specifically, this formulation offers the semi-supervised learning two characteristics: 1) unlike conventional semi-supervised learning algorithms which mostly include at least two parameters, this formulation is parameter-free; and 2) the formulation unifies the label propagation and optimal representation pursuing, and thus the label propagation is enhanced by benefiting from the graph constructed with the derived optimal representation instead of the original representation. Extensive experiments on UCI toy data, handwritten digit recognition, and face recognition all validate the effectiveness of our proposed learning framework compared with the state-ofthe- art methods for feature extraction and semi-supervised learning. © 2008 IEEE.
Source Title: Proceedings - IEEE International Conference on Data Mining, ICDM
URI: http://scholarbank.nus.edu.sg/handle/10635/70779
ISBN: 9780769535029
ISSN: 15504786
DOI: 10.1109/ICDM.2008.53
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

6
checked on Dec 14, 2017

WEB OF SCIENCETM
Citations

4
checked on Nov 20, 2017

Page view(s)

25
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.