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LABEL DISTRIBUTION LEARNING AND ITS APPLICATIONS Xin Geng (耿新) Pattern Learning and Mining (PALM) Lab (模式学习与挖掘实验室, ) School of Computer.

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Presentation on theme: "LABEL DISTRIBUTION LEARNING AND ITS APPLICATIONS Xin Geng (耿新) Pattern Learning and Mining (PALM) Lab (模式学习与挖掘实验室, ) School of Computer."— Presentation transcript:

1 LABEL DISTRIBUTION LEARNING AND ITS APPLICATIONS Xin Geng (耿新) Pattern Learning and Mining (PALM) Lab (模式学习与挖掘实验室, ) School of Computer Science and Engineering Southeast University, Nanjing, China (东南大学)

2 Learning with Ambiguity Single-label Learning Multi-label Learning Label Ambiguity Less AmbiguityMore Ambiguity ? ?

3 Label Ambiguity “What describes the instance?” cloudwaterskybuilding Multi-label Learning

4 More Ambiguity? “How to describe the instance?” some cloud much water mostly sky a bit of building

5 How to learn? MLL Thresholding Positive labels MLL Label Distribution Learning (LDL) Assign a real number to each label Importance Confidence Level …… Not a good choice! Keep more, learn more

6 LDL – Problem Formulation A real number is assigned to the label for the instance WLOG Complete label set Description Degree Label Distribution

7 LDL – Problem Formulation

8 LDL – Algorithms Two Categories Conditional Probability Mass Function (Classification) Model the mapping from the instance x to the label distribution d via a conditional PMF Multivariate Support Vector Regression (Regression) Model the mapping from the instance x to the label distribution d via a multivariate support vector machine

9 Conditional Probability Mass Function Learning from Label Distribution Training set: Goal: learn a conditional mass function that can generate label distributions similar to given the instance K-L divergence

10 Conditional Probability Mass Function Directly minimizing the K-L divergence between predicted and real LDs MaxEnt Model

11 Conditional Probability Mass Function IIS-LLD [Geng, Yin, and Zhou, TPAMI’13] [Geng, Smith-Miles, and Zhou, AAAI’10]

12 Conditional Probability Mass Function BFGS-LLD [Geng and Ji, ICDMW’13]

13 Conditional Probability Mass Function CPNN [Geng, Yin, and Zhou, TPAMI’13] 3

14 Multivariate Support Vector Regression Two issues 1. How to output a distribution composed by multiple components? Multivariate Support Vector Regression (M-SVR) [Fernandez et al., TSP’04] 2. How to constrain each component of the distribution within the range of a probability, i.e., [0, 1]? Model the regression by a sigmoid function Solve the two problems simultaneously LDSVR [Geng and Hou, submitted to IJCAI’15] Fit a sigmoid function to each component of the label distribution simultaneously by a support vector machine

15 Multivariate Support Vector Regression Sigmoid model Target function of SVR Loss Function

16 Multivariate Support Vector Regression The loss function Dimension by dimension Insensitive Zone Problem: Examples falling into the area ρ 1 will be penalized once while those falling into the area ρ 2 will be penalized twice.

17 Multivariate Support Vector Regression The loss function Multivariate Insensitive Zone Problem: Difficult to optimize and apply the kernel trick

18 Multivariate Support Vector Regression The loss function Measure the loss by calculating how far away from z i another point z′ i ∈ R c should move to get the same output with the ground truth

19 Multivariate Support Vector Regression The loss function Replacing u i with u′ i /4 Insensitive Zone

20 Age Estimation Aging is a slow and gradual progress The faces at close ages look quite similar Can we use the neighboring ages to relieve the ‘lack of training samples’ problem? [Geng, Yin, and Zhou, TPAMI’13] [Geng, Smith-Miles, and Zhou, AAAI’10]

21 Age Estimation Experiment [Geng, Yin, and Zhou, TPAMI’13] [Geng, Smith-Miles, and Zhou, AAAI’10]

22 Head Pose Estimation [Geng and Xia, CVPR’14] Bivariate Label Distribution

23 Head Pose Estimation Experiment [Geng and Xia, CVPR’14]

24 Multilabel Ranking for Natural Scene Images Multilabel Ranking A bipartition of the relevant (positive) and irrelevant (negative) labels A proper ranking over relevant labels Multiple Rankers: Subjective Inconsistent “Ground Truth” [Geng and Luo, CVPR’14]

25 Multilabel Ranking for Natural Scene Images Multilabel Ranking by Preference Distribution [Geng and Luo, CVPR’14] Virtual labels as split point between relevant and irrelevant labels

26 Multilabel Ranking for Natural Scene Images Experiment [Geng and Luo, CVPR’14]

27 Crowd Counting [Wang, Zhang and Geng, Neurocomputing’15]

28

29 Pre-release Prediction of Crowd Opinion on Movies [Geng and Hou, submitted to IJCAI’15] Crowd Rating Distribution Pre-release Metadata

30 Pre-release Prediction of Crowd Opinion on Movies Experiment [Geng and Hou, submitted to IJCAI’15]

31 Conclusion Label distribution learning More general framework than single-label and multi-label learning Deals with different importance of labels Matches certain problems better Needs special design It is useful when There is a natural measure of description degree There are multiple labeling sources for one instance The labels are correlated to each other ……

32 Interested? Download the LDL Matlab package from

33 THANK YOU palm.seu.edu.cn


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