scholarly journals Partial Label Learning via Label Enhancement

2019 ◽  
Vol 33 ◽  
pp. 5557-5564 ◽  
Author(s):  
Ning Xu ◽  
Jiaqi Lv ◽  
Xin Geng
Keyword(s):  
Predictive Model ◽  
Learning Strategy ◽  
Feature Space ◽  
Ground Truth ◽  
Learning Approaches ◽  
Topological Information ◽  
Common Strategy ◽  
Training Examples ◽  
Partial Label Learning ◽  
Label Distribution

Partial label learning aims to learn from training examples each associated with a set of candidate labels, among which only one label is valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as disambiguation by identifying the ground-truth label iteratively or disambiguation by treating each candidate label equally. Nonetheless, these strategies ignore considering the generalized label distribution corresponding to each instance since the generalized label distribution is not explicitly available in the training set. In this paper, a new partial label learning strategy named PL-LE is proposed to learn from partial label examples via label enhancement. Specifically, the generalized label distributions are recovered by leveraging the topological information of the feature space. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the generalized label distributions. Extensive experiments show that PL-LE performs favorably against state-ofthe-art partial label learning approaches.

scholarly journals Partial Multi-Label Learning with Label Distribution

2020 ◽  
Vol 34 (04) ◽  
pp. 6510-6517 ◽  
Author(s):  
Ning Xu ◽  
Yun-Peng Liu ◽  
Xin Geng
Keyword(s):  
Predictive Model ◽  
Learning Strategy ◽  
Feature Space ◽  
Ground Truth ◽  
Topological Information ◽  
Common Strategy ◽  
The Common ◽  
Real World Datasets ◽  
Training Examples ◽  
Label Distribution

Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as identifying the ground-truth label via utilizing the confidence of each candidate label or estimating the noisy labels in the candidate label sets. Nonetheless, these strategies ignore considering the essential label distribution corresponding to each instance since the label distribution is not explicitly available in the training set. In this paper, a new partial multi-label learning strategy named Pml-ld is proposed to learn from partial multi-label examples via label enhancement. Specifically, label distributions are recovered by leveraging the topological information of the feature space and the correlations among the labels. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the recovered label distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of Pml-ld for solving PML problems.

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

2022 ◽  
Vol 16 (4) ◽  
pp. 1-18
Author(s):  
Min-Ling Zhang ◽  
Jing-Han Wu ◽  
Wei-Xuan Bao
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Feature Space ◽  
Ground Truth ◽  
Learning System ◽  
Generalization Performance ◽  
Linear Discriminant ◽  
Training Examples ◽  
Partial Label Learning

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named Delin is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to k NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of Delin in improving the generalization performance of well-established partial label learning algorithms.

scholarly journals Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

2020 ◽  
Vol 34 (04) ◽  
pp. 3553-3560 ◽  
Author(s):  
Ze-Sen Chen ◽  
Xuan Wu ◽  
Qing-Guo Chen ◽  
Yao Hu ◽  
Min-Ling Zhang
Keyword(s):  
Predictive Model ◽  
Clustering Analysis ◽  
Experimental Studies ◽  
Ground Truth ◽  
Two Stage ◽  
Feature Vectors ◽  
Similarity Graph ◽  
Training Examples ◽  
Guided Clustering

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

scholarly journals Partial Label Learning with Self-Guided Retraining

2019 ◽  
Vol 33 ◽  
pp. 3542-3549 ◽  
Author(s):  
Lei Feng ◽  
Bo An
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Confidence ◽  
Infinity Norm ◽  
Real World Datasets ◽  
Partial Label Learning ◽  
Optimization Efficiency

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

scholarly journals Label Enhancement for Label Distribution Learning

2018 ◽  
Author(s):  
Ning Xu ◽  
An Tao ◽  
Xin Geng
Keyword(s):  
Learning Process ◽  
Real World ◽  
Feature Space ◽  
Graph Laplacian ◽  
Training Set ◽  
Topological Information ◽  
Label Distribution Learning ◽  
Real World Datasets ◽  
Label Distribution ◽  
Training Sets

Label distribution is more general than both single-label annotation and multi-label annotation. It covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly.  To solve the problem, one way is to recover the label distributions from the logical labels in the training set via leveraging the topological information of the feature space and the correlation among the labels. Such process of recovering label distributions from logical labels is defined as label enhancement (LE), which reinforces the supervision information in the training sets. This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE). Experimental results on one artificial dataset and fourteen real-world datasets show clear advantages of GLLE over several existing LE algorithms.

scholarly journals Towards Enabling Binary Decomposition for Partial Label Learning

2018 ◽  
Author(s):  
Xuan Wu ◽  
Min-Ling Zhang
Keyword(s):  
Learning Algorithm ◽  
Experimental Studies ◽  
Ground Truth ◽  
Binary Classifier ◽  
Decomposition Approach ◽  
Novel Approach ◽  
Binary Decomposition ◽  
Binary Classifiers ◽  
Training Examples ◽  
Partial Label Learning

The task of partial label (PL) learning is to learn a multi-class classifier from training examples each associated with a set of candidate labels, among which only one corresponds to the ground-truth label. It is well known that for inducing multi-class predictive model, the most straightforward solution is binary decomposition which works by either one-vs-rest or one-vs-one strategy. Nonetheless, the ground-truth label for each PL training example is concealed in its candidate label set and thus not accessible to the learning algorithm, binary decomposition cannot be directly applied under partial label learning scenario. In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy. Specifically, one binary classifier is derived for each pair of class labels, where PL training examples with distinct relevancy to the label pair are used to generate the corresponding binary training set. After that, one binary classifier is further derived for each class label by stacking over predictions of existing binary classifiers to improve generalization. Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w.r.t state-of-the-art partial label learning techniques.

scholarly journals The Active Segmentation Platform for Microscopic Image Classification and Segmentation

2021 ◽  
Vol 11 (12) ◽  
pp. 1645
Author(s):  
Sumit K. Vohra ◽  
Dimiter Prodanov
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Image Classification ◽  
Domain Knowledge ◽  
Feature Space ◽  
Ground Truth ◽  
Classification Problem ◽  
Data Sets ◽  
Learning Approaches ◽  
Data Set

Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects.

scholarly journals Visibility Estimation via Deep Label Distribution Learning

2021 ◽  
Author(s):  
Mofei Song ◽  
Han Xu ◽  
Xiao Fan Liu ◽  
Qian Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Conditional Probability ◽  
Estimation Method ◽  
Ground Truth ◽  
Learning Performance ◽  
Label Distribution Learning ◽  
Training Examples ◽  
Label Distribution ◽  
Visibility Estimation

This paper proposes an image-based visibility estimation method with deep label distribution learning. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. Our experiment shows that labeling the image with visibility distribution can not only overcome the inaccurate annotation problem, but also boost the learning performance without the increase of training examples.

scholarly journals Tensor-based multi-view label enhancement for multi-label learning

2020 ◽  
Author(s):  
Fangwen Zhang ◽  
Xiuyi Jia ◽  
Weiwei Li
Keyword(s):  
Topological Structure ◽  
Semantic Information ◽  
Feature Space ◽  
Tensor Factorization ◽  
Topological Information ◽  
Single View ◽  
Real World Applications ◽  
Enhancement Method ◽  
The Common ◽  
Label Distribution

Label enhancement (LE) is a procedure of recovering the label distributions from the logical labels in the multi-label data, the purpose of which is to better represent and mine the label ambiguity problem through the form of label distribution. Existing LE work mainly concentrates on how to leverage the topological information of the feature space and the correlation among the labels, and all are based on single view data. In view of the fact that there are many multi-view data in real-world applications, which can provide richer semantic information from different perspectives, this paper first presents a multi-view label enhancement problem and proposes a tensor-based multi-view label enhancement method, named TMV-LE. Firstly, we introduce the tensor factorization to get the common subspace which contains the high-order relationships among different views. Secondly, we use the common representation and multiple views to jointly mine a more comprehensive topological structure in the dataset. Finally, the topological structure of the feature space is migrated to the label space to get the label distributions. Extensive comparative studies validate that the performance of multi-view multi-label learning can be improved significantly with TMV-LE.

scholarly journals Visibility Estimation via Deep Label Distribution Learning

2021 ◽  
Author(s):  
Mofei Song ◽  
Han Xu ◽  
Xiao Fan Liu ◽  
Qian Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Conditional Probability ◽  
Estimation Method ◽  
Ground Truth ◽  
Learning Performance ◽  
Label Distribution Learning ◽  
Training Examples ◽  
Label Distribution ◽  
Visibility Estimation

This paper proposes an image-based visibility estimation method with deep label distribution learning. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. Our experiment shows that labeling the image with visibility distribution can not only overcome the inaccurate annotation problem, but also boost the learning performance without the increase of training examples.

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