Book ChapterDOI
Label Correlation Propagation for Semi-supervised Multi-label Learning
Aritra Ghosh,C. Chandra Sekhar +1 more
- pp 52-60
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TLDR
This paper proposes two different graph based transductive methods, namely, the label correlation propagation and the k-nearest neighbors based label correlation propagate, for semi-supervised multi-label learning.Abstract:
Many real world machine learning tasks suffer from the problem of scarce labeled data. In multi-label learning, each instance is associated with more than one label as in semantic scene understanding, text categorization and bio-informatics. Semi-supervised multi-label learning has attracted recent interest as gathering labeled data is both expensive and requires manual effort. Further, many of the labels have semantic correlation which manifests as co-occurrence and this information can be used to build effective classifiers in the multi-label scenario. In this paper, we propose two different graph based transductive methods, namely, the label correlation propagation and the k-nearest neighbors based label correlation propagation. Extensive experimentation on real-world datasets demonstrates the efficacy of the proposed methods and the importance of using the label correlation information in semi-supervised multi-label learning.read more
Citations
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Journal ArticleDOI
A survey of multi-label classification based on supervised and semi-supervised learning
Book ChapterDOI
Multi-label K-Nearest Neighbor Classification Method Based on Semi-supervised
TL;DR: SSML-kNN firstly proposed a semi-supervised self-training model, and multi-label k-nearest neighbor classification based on correlation degree is used to classify the unlabeled datasets.
References
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TL;DR: A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points.
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TL;DR: 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.
Journal ArticleDOI
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
Journal ArticleDOI
Learning multi-label scene classification
TL;DR: A framework to handle semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels, is presented and appears to generalize to other classification problems of the same nature.