Cited By
View all- Tan CChen SJi GGeng X(2022)Multilabel Distribution Learning Based on Multioutput Regression and Manifold LearningIEEE Transactions on Cybernetics10.1109/TCYB.2020.302657652:6(5064-5078)Online publication date: Jun-2022
Unleash the Power of Label Space: Label Enhancement for Label Distribution Learning
Author: 
Xin GengAuthors Info & Claims
Page 7
Abstract
In the existing machine learning literature, the labels of the training examples are usually just used in the calculation of loss. Most sophisticated operations are actually conducted on the instances, such as feature extraction, feature selection, manifold embedding, dimensionality reduction, etc. Researchers take obviously more efforts in the feature space than in the label space, which is not strange since labels are traditionally represented by logical values, i.e., 1 if the label is relevant to the instance and 0 otherwise. However, if we can somehow transform the logical label vectors into real-valued label vectors, then we can expect much more profound analysis in the label space.
Label distribution learning (LDL) is a recently proposed machine learning paradigm, where each instance is labeled by a real-valued label vector called label distribution. Each element in the label distribution indicates the description degree of the corresponding label to the instance. Considering most existing data sets are annotated by logical labels, we need a way to transform logical labels into label distributions, which is called label enhancement. Label enhancement could unleash the power of label space: many analytic operations meant for the feature space are now applicable to the label space.
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Published In
December 2021
698 pages
ISBN:9781450391153
DOI:10.1145/3486622
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Publication History
Published: 13 April 2022
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WI-IAT '21
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WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence
December 14 - 17, 2021
VIC, Melbourne, Australia
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Cited By
View all- Tan CChen SJi GGeng X(2022)Multilabel Distribution Learning Based on Multioutput Regression and Manifold LearningIEEE Transactions on Cybernetics10.1109/TCYB.2020.302657652:6(5064-5078)Online publication date: Jun-2022
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