Label Propagation through Linear Neighborhoods

@article{Wang2006LabelPT,
  title={Label Propagation through Linear Neighborhoods},
  author={Fei Wang and Changshui Zhang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2006},
  volume={20},
  pages={55-67}
}
In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervised learning algorithms have aroused considerable interests from the data mining and machine learning fields. In recent years, graph-based semi supervised learning has been becoming one of the most active research areas in the semi supervised learning community. In this paper, a novel graph-based semi… 

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References

SHOWING 1-10 OF 54 REFERENCES

Learning from Labeled and Unlabeled Data Using Random Walks

This work investigates the proposed algorithm which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

An approach to semi-supervised learning is proposed that is based on a Gaussian random field model, and methods to incorporate class priors and the predictions of classifiers obtained by supervised learning are discussed.

Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification

A Bayesian framework for learning hyperparameters for graph-based semi-supervised classification and shows that the posterior mean can be written in terms of the kernel matrix, providing a Bayesian classifier to classify new points.

Efficient Non-Parametric Function Induction in Semi-Supervised Learning

Experiments show that the proposed non-parametric algorithms which provide an estimated continuous label for the given unlabeled examples are extended to function induction algorithms that correspond to the minimization of a regularization criterion applied to an out-of-sample example, and happens to have the form of a Parzen windows regressor.

Text Classification from Labeled and Unlabeled Documents using EM

This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.

Learning from Labeled and Unlabeled Data using Graph Mincuts

An algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data is considered.

Combining labeled and unlabeled data with co-training

A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.

Tikhonov regularization and semi-supervised learning on large graphs

Using the notion of algorithmic stability, bounds on the generalization error are derived and related to the structural invariants of the graph and a framework for regularization is developed parallel to Tikhonov regularization on continuous spaces.

Partially labeled classification with Markov random walks

This work combines a limited number of labeled examples with a Markov random walk representation over the unlabeled examples and develops and compares several estimation criteria/algorithms suited to this representation.

Learning from labeled and unlabeled data with label propagation

A simple iterative algorithm to propagate labels through the dataset along high density are as d fined by unlabeled data is proposed and its solution is analyzed, and its connection to several other algorithms is analyzed.
...