Provably Consistent Partial-Label Learning

NeurIPS, 2020.

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Extensive experimental results clearly demonstrated the effectiveness of the proposed generation model and two partial-label learning methods

Abstract:

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto po...More

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Introduction
  • Unlike supervised learning and unsupervised learning, weakly supervised learning [1] aims to learn with weak supervision.
  • Examples include semi-supervised learning [2, 3, 4], multi-instance learning [5, 6], positive-unlabeled learning [7, 8], complementary-label learning [9, 10], noisy-label learning [11, 12, 13], positive-confidence learning [14], similarunlabeled learning [15], and unlabeled-unlabeled learning [16, 17]
  • In recent years, another weakly supervised learning framework called partial-label learning (PLL) [18, 19, 20, 21, 22, 23, 24] has gradually attracted attention from machine learning and data mining communities.
  • Due to the difficulty in collecting accurately labeled data in many real-world scenarios, PLL has been successfully applied to a wide range of application domains, such as web mining [31], bird song classification [29], and automatic face naming [26]
Highlights
  • Unlike supervised learning and unsupervised learning, weakly supervised learning [1] aims to learn with weak supervision
  • partial-label learning (PLL) aims to deal with the problem where each instance is provided with a set of candidate labels, only one of which is the correct label
  • We find that the candidate label sets with higher entropy better match our generation model, and on such datasets, our proposed PLL methods achieve better performance
  • We further show via experiments that even when given candidate label sets do not match our proposed generation model well, our methods still significantly outperform other compared methods
  • To the best of our knowledge, we provided the first risk-consistent PLL method
  • Extensive experimental results clearly demonstrated the effectiveness of the proposed generation model and two PLL methods
Methods
  • Based on the assumed partially labeled data distribution in Eq (5), the authors present a novel risk-consistent method and a novel classifier-consistent method, and theoretically derive an estimator error bound for each of them
  • Both of the consistent methods are agnostic in specific classification models and can be trained with stochastic optimization, which ensures their scalability to large-scale datasets.
Results
  • The authors can observe that RC always achieves the best performance and significantly outperforms other compared methods in most cases.
  • The authors further show via experiments that even when given candidate label sets do not match the proposed generation model well, the methods still significantly outperform other compared methods
Conclusion
  • The authors for the first time provided an explicit mathematical formulation of the partially labeled data generation process for PLL.
  • Based on the data generation model, the authors further derived a novel risk-consistent method and a novel classifier-consistent method.
  • To the best of the knowledge, the authors provided the first risk-consistent PLL method.
  • The authors theoretically derived an estimation error bound for each of the proposed methods.
  • Extensive experimental results clearly demonstrated the effectiveness of the proposed generation model and two PLL methods
Summary
  • Introduction:

    Unlike supervised learning and unsupervised learning, weakly supervised learning [1] aims to learn with weak supervision.
  • Examples include semi-supervised learning [2, 3, 4], multi-instance learning [5, 6], positive-unlabeled learning [7, 8], complementary-label learning [9, 10], noisy-label learning [11, 12, 13], positive-confidence learning [14], similarunlabeled learning [15], and unlabeled-unlabeled learning [16, 17]
  • In recent years, another weakly supervised learning framework called partial-label learning (PLL) [18, 19, 20, 21, 22, 23, 24] has gradually attracted attention from machine learning and data mining communities.
  • Due to the difficulty in collecting accurately labeled data in many real-world scenarios, PLL has been successfully applied to a wide range of application domains, such as web mining [31], bird song classification [29], and automatic face naming [26]
  • Methods:

    Based on the assumed partially labeled data distribution in Eq (5), the authors present a novel risk-consistent method and a novel classifier-consistent method, and theoretically derive an estimator error bound for each of them
  • Both of the consistent methods are agnostic in specific classification models and can be trained with stochastic optimization, which ensures their scalability to large-scale datasets.
  • Results:

    The authors can observe that RC always achieves the best performance and significantly outperforms other compared methods in most cases.
  • The authors further show via experiments that even when given candidate label sets do not match the proposed generation model well, the methods still significantly outperform other compared methods
  • Conclusion:

    The authors for the first time provided an explicit mathematical formulation of the partially labeled data generation process for PLL.
  • Based on the data generation model, the authors further derived a novel risk-consistent method and a novel classifier-consistent method.
  • To the best of the knowledge, the authors provided the first risk-consistent PLL method.
  • The authors theoretically derived an estimation error bound for each of the proposed methods.
  • Extensive experimental results clearly demonstrated the effectiveness of the proposed generation model and two PLL methods
Tables
  • Table1: Test performance (mean±std) of each method using neural networks on benchmark datasets. ResNet is trained on CIFAR-10, and MLP is trained on the other three datasets
  • Table2: Test performance (mean±std) of each method using neural networks on benchmark datasets. DenseNet is trained on CIFAR-10, and LeNet is trained on the other three datasets
  • Table3: Test performance (mean±std) of each method using linear model on UCI datasets
  • Table4: Test performance (mean±std) of each method using linear model on real-world datasets
  • Table5: Characteristics of the controlled datasets
  • Table6: Characteristics of the real-world partially labeled datasets
  • Table7: Transductive accuracy of each method using neural networks on benchmark datasets. ResNet is trained on CIFAR-10, and MLP is trained on the other three datasets
  • Table8: Transductive accuracy of each method using neural networks on benchmark datasets. DenseNet is trained on CIFAR-10, and LeNet is trained on the other three datasets
  • Table9: Test performance (mean±std) of the RC method using neural networks on benchmark datasets with different generation models. The best performance is highlighted in bold
  • Table10: Test performance (mean±std) of the CC method using neural networks on benchmark datasets with different generation models. The best performance is highlighted in bold
  • Table11: Test performance (mean±std) of each method using neural networks on benchmark datasets. DenseNet is trained on CIFAR-10, and LeNet is trained on the other three datasets. Candidate label sets are generated by the generation model in Case 1 (entropy=2.015)
Download tables as Excel
Funding
  • BH was supported by the Early Career Scheme (ECS) through the Research Grants Council of Hong Kong under Grant No.22200720, HKBU Tier-1 Start-up Grant, and HKBU CSD Start-up Grant
  • GN and MS were supported by JST AIP Acceleration Research Grant Number JPMJCR20U3, Japan
Study subjects and analysis
widely used benchmark datasets: 4
Datasets. We collect four widely used benchmark datasets including MNIST [43], Kuzushiji-MNIST [44], Fashion-MNIST [45], and CIFAR-10 [46], and five datasets from the UCI Machine Learning Repository [46]. In order to generate candidate label sets on these datasets, following the motivation in Section 3.2, we uniformly sample the candidate label set that includes the correct label from C for each instance

benchmark datasets: 4
Experimental Results. We run 5 trials on the four benchmark datasets and run 10 trials (with 90%/10% train/test split) on UCI datasets and real-world partially labeled datasets, and record the mean accuracy with standard deviation (mean±std). We also use paired t-test at 5% significance level, and •/◦ represents whether the best of RC and CC is significantly better/worse than other compared methods

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