Article

Label propagation through linear neighborhoods

Authors:

Changshui ZhangAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 985 - 992

https://doi.org/10.1145/1143844.1143968

Published: 25 June 2006 Publication History

Abstract

A novel semi-supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named Linear Neighborhood Propagation (LNP), can propagate the labels from the labeled points to the whole dataset using these linear neighborhoods with sufficient smoothness. We also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit and text classification tasks.

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Index Terms

Label propagation through linear neighborhoods
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

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https://doi.org/10.1109/TGRS.2025.3528464
Wen JChen XKong HZhang JLai ZShen L(2025)Subspace Sequentially Iterative Leaning for Semi-Supervised SVMIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.34059109:1(681-694)Online publication date: Feb-2025
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