scholarly journals Prediction in the Presence of Response-Dependent Missing Labels

2021 ◽  
Author(s):  
Hyebin Song ◽  
Garvesh Raskutti ◽  
Rebecca Willett
Keyword(s):  
Missing Labels

SVM based multi-label learning with missing labels for image annotation

2018 ◽  
Vol 78 ◽  
pp. 307-317 ◽  
Author(s):  
Yang Liu ◽  
Kaiwen Wen ◽  
Quanxue Gao ◽  
Xinbo Gao ◽  
Feiping Nie
Keyword(s):  
Image Annotation ◽  
Missing Labels

Feature selection for multi-label learning with missing labels

2019 ◽  
Vol 49 (8) ◽  
pp. 3027-3042 ◽  
Author(s):  
Chenxi Wang ◽  
Yaojin Lin ◽  
Jinghua Liu
Keyword(s):  
Feature Selection ◽  
Missing Labels ◽  
Selection For

scholarly journals Leveraging Known Data for Missing Label Prediction in Cultural Heritage Context

2018 ◽  
Vol 8 (10) ◽  
pp. 1768 ◽  
Author(s):  
Abdelhak Belhi ◽  
Abdelaziz Bouras ◽  
Sebti Foufou
Keyword(s):  
Neural Network ◽  
Cultural Heritage ◽  
Feature Learning ◽  
Image Data ◽  
Heritage Sites ◽  
Multimodal Classification ◽  
Label Prediction ◽  
Missing Labels ◽  
Traditional Approaches ◽  
Better Than

Cultural heritage represents a reliable medium for history and knowledge transfer. Cultural heritage assets are often exhibited in museums and heritage sites all over the world. However, many assets are poorly labeled, which decreases their historical value. If an asset’s history is lost, its historical value is also lost. The classification and annotation of overlooked or incomplete cultural assets increase their historical value and allows the discovery of various types of historical links. In this paper, we tackle the challenge of automatically classifying and annotating cultural heritage assets using their visual features as well as the metadata available at hand. Traditional approaches mainly rely only on image data and machine-learning-based techniques to predict missing labels. Often, visual data are not the only information available at hand. In this paper, we present a novel multimodal classification approach for cultural heritage assets that relies on a multitask neural network where a convolutional neural network (CNN) is designed for visual feature learning and a regular neural network is used for textual feature learning. These networks are merged and trained using a shared loss. The combined networks rely on both image and textual features to achieve better asset classification. Initial tests related to painting assets showed that our approach performs better than traditional CNNs that only rely on images as input.

Multi-label learning with missing labels for image annotation and facial action unit recognition

2015 ◽  
Vol 48 (7) ◽  
pp. 2279-2289 ◽  
Author(s):  
Baoyuan Wu ◽  
Siwei Lyu ◽  
Bao-Gang Hu ◽  
Qiang Ji
Keyword(s):  
Image Annotation ◽  
Action Unit ◽  
Facial Action ◽  
Missing Labels

scholarly journals Prognostic Model Development with Missing Labels

2019 ◽  
Vol 61 (3) ◽  
pp. 327-343
Author(s):  
Patrick Zschech ◽  
Kai Heinrich ◽  
Raphael Bink ◽  
Janis S. Neufeld
Keyword(s):  
Prognostic Model ◽  
Model Development ◽  
Missing Labels
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