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A Novel Probabilistic Label Enhancement Algorithm for Multi-Label Distribution Learning
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DOI：10.1109/TKDE.2021.3054465.png)
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摘要
En 中文
We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks.
Keyword:
Feature extraction
Manifolds
Entropy
Training
Videos
Prediction algorithms
Manifold learning
Multi-label classification
label distribution learning
manifold learning
robust linear regression
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10.4
论文数:
6.9K
被引数:
3.2W
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Principal manifolds and nonlinear dimensionality reduction via tangent space alignment基于切线空间对齐的主流形和非线性降维
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