Journal ArticleDOI
Graph-based semi-supervised learning with multiple labels
TLDR
A novel graph-based learning framework in the setting of semi-supervised learning with multiple labels is proposed, characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2009-02-01. It has received 156 citations till now. The article focuses on the topics: Graph (abstract data type) & Semi-supervised learning.read more
Citations
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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
Tag Completion for Image Retrieval
Lei Wu,Rong Jin,Anil K. Jain +2 more
TL;DR: This work proposes a new algorithm for tag completion, where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images and represents the image-tag relation by a tag matrix, and search for the optimal tag matrix consistent with both the observed tags and the visual similarity.
Journal ArticleDOI
Multi-label learning: a review of the state of the art and ongoing research
TL;DR: The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.
Journal ArticleDOI
Active deep learning method for semi-supervised sentiment classification
TL;DR: Experiments on five sentiment classification datasets show that ADN and IADN outperform classical semi-supervised learning algorithms, and deep learning techniques applied for sentiment classification.
Journal ArticleDOI
Interactive Video Indexing With Statistical Active Learning
TL;DR: A novel active learning approach based on the optimum experimental design criteria in statistics is proposed that simultaneously exploits sample's local structure, and sample relevance, density, and diversity information, as well as makes use of labeled and unlabeled data.
References
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Book
The Theory of Matrices
TL;DR: In this article, the Routh-Hurwitz problem of singular pencils of matrices has been studied in the context of systems of linear differential equations with variable coefficients, and its applications to the analysis of complex matrices have been discussed.
Proceedings ArticleDOI
Combining labeled and unlabeled data with co-training
Avrim Blum,Tom M. Mitchell +1 more
TL;DR: 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.
Proceedings Article
Learning with Local and Global Consistency
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.