This paper proposes a novel multi-label learning algorithm named MMFL, i.e., Multi-label learning with Missing Features and Labels, which can deal with the problem of missing features and labels simultaneously. First, we try to recover the missing values of features and labels by matrix ...
Paper2:《Simple and Robust Loss Design for Multi-Label Learning with Missing Labels》 ArXiv 2021. 第一篇是做每张图只有一个类别打上了pos label,其他类别都没给label。 本文从梯度的角度,提出了熵最大化loss,目的是为了让没有标注的label预测可以更不确定一些。 如下图所示,针对该问题,最简单的bsl是我...
Interactive Multi-Label CNN Learning with Partial Labels IMLC, CVPR, 2020 考虑一般的 cross-entropy 对 missing label 的情况不是很鲁棒,本文通过学习label-instace的相似度图对 cross-entropy 进行正则,缓解missing label 的影响;并且本文的方法是 interactive 的,也就是说分类器和学习相似度图这两个过程是想...
Published by Journal of Machine Learning Research Download BibTex The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions)...
摘要 In multi-label learning, feature selection is a non-ignorable preprocessing step which can alleviate the negative eff... 出版源 Springer US , 2019 关键词 Feature interaction / Feature selection / Missing labels / Multi-label le...
In this paper, we develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels. Specifically, we impose the low-rank structures on all the predictions of instances from the same labels (local shrinking of rank), and a maximally ...
# Mask for missing label.mask_value=-1# Drop 2% y0.Y[:int(N*0.020),0]=mask_value# Drop 0.7% y1.Y[int(N*0.018):int(N*0.0025),1]=mask_value# Drop 1.1% y2.Y[int(N*0.024):int(N*0.0035),2]=mask_value Let's build a simple model with 4 layers, ...
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To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years. Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification. Although ...
Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the sa...