Graph construction and
            <i>b</i>
            -matching for semi-supervised learning

Jebara, Tony; Wang, Jun; Chang, Shih-Fu

doi:10.1145/1553374.1553432

Cited by 263 publications

(249 citation statements)

References 12 publications

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“…There are two popular graph sparsification algorithms as summarized in [22]: neighborhood sparsification such as k-nearest neighbors (k-NN) and -neighborhood, and matching sparsification. The former often suffers from the issue that nodes in dense regions may have too many links.…”

Section: A Graph Constructionmentioning

confidence: 99%

“…The former often suffers from the issue that nodes in dense regions may have too many links. The latter explicitly addresses this issue by adding a constraint that every node has the same number of edge links, leading to much improved performances as reported in [22].…”

Section: A Graph Constructionmentioning

confidence: 99%

“…0. Three possible schemes, including binary weighting, Gaussian kernel weighting [22], and locally linear reconstruction-based weighting [50], are commonly considered for specifying the nonzero elements in W.…”

Section: A Graph Constructionmentioning

confidence: 99%

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Robust and Scalable Graph-Based Semisupervised Learning

2012

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Graph-based semisupervised learning methods and new techniques for handling contaminated noisy labels, and gigantic data sizes for web applications, are reviewed in this paper.By Wei Liu, Jun Wang, Member IEEE, and Shih-Fu Chang, Fellow IEEE ABSTRACT | Graph-based semisupervised learning (GSSL) provides a promising paradigm for modeling the manifold structures that may exist in massive data sources in highdimensional spaces. It has been shown effective in propagating a limited amount of initial labels to a large amount of unlabeled data, matching the needs of many emerging applications such as image annotation and information retrieval. In this paper, we provide reviews of several classical GSSL methods and a few promising methods in handling challenging issues often encountered in web-scale applications. First, to successfully incorporate the contaminated noisy labels associated with web data, label diagnosis and tuning techniques applied to GSSL are surveyed. Second, to support scalability to the gigantic scale (millions or billions of samples), recent solutions based on anchor graphs are reviewed. To help researchers pursue new ideas in this area, we also summarize a few popular data sets and software tools publicly available. Important open issues are discussed at the end to stimulate future research.

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Section: A Graph Constructionmentioning

confidence: 99%

Section: A Graph Constructionmentioning

confidence: 99%

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Robust and Scalable Graph-Based Semisupervised Learning

2012

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“…Previous works on automatic image annotation using graph-based SSL can be classified into two major technical components: (1) graph construction [4,5,6,7,8,9], and (2) label propagation [10,11,12,13,14,15,16,17,18,19].…”

Section: Previous Workmentioning

confidence: 99%

“…ε. In contrast, Jebara et al argued the importance to form a regular graph followed by presenting a regular-graph construction approach based on b-matching [5]. However, the use of b-matching may result in a high complexity which may not be practical to large-scale datasets.…”

Section: Previous Workmentioning

confidence: 99%

Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets

Lee

Hsieh

et al. 2013

Journal of Visual Communication and Image Representation

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Semi-supervised learning (SSL) is widely-used to explore the vast amount of unlabeled data in the world. Over the decade, graph-based SSL becomes popular in automatic image annotation due to its power of learning globally based on local similarity. However, recent studies have shown that the emergence of large-scale datasets challenges the traditional methods. On the other hand, most previous works have concentrated on single-label annotation, which may not describe image contents well. To remedy the deficiencies, this paper proposes a new graph-based SSL technique with multi-label propagation, leveraging the distributed computing power of the MapReduce programming model. For high learning performance, the paper further presents both a multi-layer learning structure and a tag refinement approach, where the former unifies both visual and textual information of image data during learning, while the latter simultaneously suppresses noisy tags and emphasizes the other tags after learning. Experimental results based on a medium-scale and a large-scale image datasets show the effectiveness of the proposed methods.

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GRAW+: A two‐view graph propagation method with word coupling for readability assessment

Jiang

Yin

et al. 2019

Asso for Info Science & Tech

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Existing methods for readability assessment usually construct inductive classification models to assess the readability of singular text documents based on extracted features, which have been demonstrated to be effective. However, they rarely make use of the interrelationship among documents on readability, which can help increase the accuracy of readability assessment. In this article, we adopt a graphbased classification method to model and utilize the relationship among documents using the coupled bag-of-words model. We propose a word coupling method to build the coupled bag-of-words model by estimating the correlation between words on reading difficulty. In addition, we propose a two-view graph propagation method to make use of both the coupled bag-of-words model and the linguistic features. Our method employs a graph merging operation to combine graphs built according to different views, and improves the label propagation by incorporating the ordinal relation among reading levels. Experiments were conducted on both English and Chinese data sets, and the results demonstrate both effectiveness and potential of the method.

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Graph construction and b -matching for semi-supervised learning

Cited by 263 publications

References 12 publications

Robust and Scalable Graph-Based Semisupervised Learning

Robust and Scalable Graph-Based Semisupervised Learning

Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets

GRAW+: A two‐view graph propagation method with word coupling for readability assessment

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