A New SVM Approach to Multi-instance Multi-label Learning
A New SVM Approach to Multi-instance Multi-label Learning
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DOI：
10.1109/icdm.2010.109
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发表时间：
2010-12
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通讯作者：
Nam Nguyen
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作者：
Nam Nguyen
在本文中，我们解决了多实例多标签学习（MIML）的问题，其中每个例子不仅与多个实例，而且与多个类标签。在我们的新方法中，给定MIML示例，示例中的每个实例仅与单个标签相关联，并且示例的标签集是所有实例标签的聚合。许多现实世界的任务，如场景分类，文本分类和基因序列编码可以适当的形式化下，我们提出的方法。我们将MIML问题表述为两种优化的组合：（1）使用L2范数正则化最小化经验风险的二次规划（QP），以及（2）将每个实例分配给单个标签的整数规划（IP）。我们还提出了一个有效的方法相结合的随机梯度下降和交替优化方法来解决我们的QP和IP优化。在我们的实验与人工生成的数据集和现实世界的应用，即场景分类和文本分类，我们提出的方法实现了上级性能优于现有的国家的最先进的MIML方法，如MIMLBOOST，MIMLSVM，M$^3$MIML和MIMLRBF。
In this paper, we address the problem of multi-instance multi-label learning (MIML) where each example is associated with not only multiple instances but also multiple class labels. In our novel approach, given an MIML example, each instance in the example is only associated with a single label and the label set of the example is the aggregation of all instance labels. Many real-world tasks such as scene classification, text categorization and gene sequence encoding can be properly formalized under our proposed approach. We formulate our MIML problem as a combination of two optimizations: (1) a quadratic programming (QP) that minimizes the empirical risk with L2-norm regularization, and (2) an integer programing (IP) assigning each instance to a single label. We also present an efficient method combining the stochastic gradient decent and alternating optimization approaches to solve our QP and IP optimizations. In our experiments with both an artificially generated data set and real-world applications, i.e. scene classification and text categorization, our proposed method achieves superior performance over existing state-of-the-art MIML methods such as MIMLBOOST, MIMLSVM, M$^3$MIML and MIMLRBF.