Binary Classification with Classical Instances and Quantum Labels

Learning from Label Proportions with Instance-wise Consistency

Learning from Label Proportions (LLP) is a weakly supervised learning method that aims to perform instance classification from training data consisting of pairs of bags containing multiple instances and the class label proportions within the bags. Previous studies on multiclass LLP can be divided into two categories according to the learning task: per-instance label classification and per-bag label proportion estimation. However, these methods often results in high variance estimates of the risk when applied to complex models, or lack statistical learning theory arguments. To address this issu…

Active Learning in Incomplete Label Multiple Instance Multiple Label Learning

In multiple instance multiple label learning, each sample, a bag, consists of multiple instances. To alleviate labeling complexity, each sample is associated with a set of bag-level labels leaving instances within the bag unlabeled. This setting is more convenient and natural for representing complicated objects, which have multiple semantic meanings. Compared to single instance labeling, this approach allows for labeling larger datasets at an equivalent labeling cost. However, for sufficiently large datasets, labeling all bags may become prohibitively costly. Active learning uses an iterative…

从标签比例与协变量偏移的实例中学习

Learning from Label Proportions and Covariate-shifted Instances

本文研究协变量偏移的混合 LLP 问题，在领域适应框架下提出新方法，将目标包标签与源实例标签结合以训练实例级预测器，理论证明目标泛化误差上界，并在公开数据集实验中优于 LLP 和领域适应基线。

Fast Multi-Instance Multi-Label Learning

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which firs…

Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport

Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as multiple annotations. Previous methods assume that the homogeneous multi-modal data are consistent, while in real applications, the raw data are disordered, e.g., the article constitutes with variable number of inconsistent text and image instances. Therefore, Multi-modal Multi-instance Multi-label (M3) learning provides a framework for handling such task and has exhibited excellent performance. However, M3 learning…

基于二进制哈希学习和动态图约束的多标签特征选择

Multi-label feature selection based on binary hashing learning and dynamic graph constraints

本文提出BHDG方法，通过二进制哈希码和动态图约束提升多标签学习中噪声抑制和特征选择性能，实验表明其在六个评估指标上均优于现有方法。