面向多标签分类的弱监督卷积字典学习
Weakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
AI总结：
针对仅含包级标注的弱监督多示例多标签分类问题，提出联合学习共享与类别特定字典的卷积字典学习框架，采用BPG-M算法优化，性能优于现有方法，适用于声音、射频信号等分类场景。
摘要：
卷积字典学习（Convolutional Dictionary Learning, CDL）是一种强大的信号表征方法，通过卷积操作学习平移不变特征。现有CDL方法大多针对全监督场景设计和使用，但许多现实分类任务往往依赖弱标记数据，这类数据仅提供包级别的标注。本文提出一种新型弱监督卷积字典学习框架，用于多示例多标签（multi-instance multi-label, MIML）分类任务——该任务中每个样本包含多个示例，且可能对应多个标签，框架可联合学习共享组件与类别特定组件。我们的方法将信号分解为共享字典捕获的背景模式，以及类别特定字典编码的判别性特征，并通过核范数约束防止特征稀释。我们开发了带优化的块近端梯度法（Block Proximal Gradient method with Majorization, BPG-M），用于交替更新字典原子与稀疏系数，确保收敛至局部最小值。此外，我们引入了一种投影机制，通过可学习的池化算子将示例级预测聚合为包级标签。在合成数据集与真实数据集上的实验结果表明，我们的框架在分类性能上优于现有MIML方法，在低标记场景下优势尤为明显。学习得到的字典可提供可解释的表征，同时能有效处理背景噪声与长度可变的示例，因此该方法适用于环境声音分类、射频信号分析等应用场景。
英文摘要：
Convolutional Dictionary Learning (CDL) has emerged as a powerful approach for signal representation by learning translation-invariant features through convolution operations. While existing CDL methods are predominantly designed and used for fully supervised settings, many real-world classification tasks often rely on weakly labeled data, where only bag-level annotations are available. In this paper, we propose a novel weakly supervised convolutional dictionary learning framework that jointly learns shared and class-specific components, for multi-instance multi-label (MIML) classification where each example consists of multiple instances and may be associated with multiple labels. Our approach decomposes signals into background patterns captured by a shared dictionary and discriminative features encoded in class-specific dictionaries, with nuclear norm constraints preventing feature dilution. A Block Proximal Gradient method with Majorization (BPG-M) is developed to alternately update dictionary atoms and sparse coefficients, ensuring convergence to local minima. Furthermore, we incorporate a projection mechanism that aggregates instance-level predictions to bag-level labels through learnable pooling operators.Experimental results on both synthetic and real-world datasets demonstrate that our framework outperforms existing MIML methods in terms of classification performance, particularly in low-label regimes. The learned dictionaries provide interpretable representations while effectively handling background noise and variable-length instances, making the method suitable for applications such as environmental sound classification and RF signal analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
