Unsupervised feature optimization (UFO): Simultaneous selection of multiple features with their detection parameters
Unsupervised feature optimization (UFO): Simultaneous selection of multiple features with their detection parameters
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无监督特征优化（UFO）：同时选择多个特征及其检测参数
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
S. Ullman
中科院分区：
文献类型：
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作者：
Leonid Karlinsky;Michael Dinerstein;S. Ullman
课堂学习，无论是有监督的还是无监督的，都需要特征选择，其中包括两个主要组成部分。第一个是从更大的池中选择有区别的特征子集。其次是为每个特征选择检测参数以优化分类性能。在本文中，我们提出了一种在完全无监督的情况下发现多个分类特征、它们的检测参数及其一致配置的方法。这是通过将特征之间的联合一致性作为检测参数的函数进行全局优化来实现的，而不需要假设任何先前的参数模型。我们演示了如何应用所提出的框架来学习不同类型的特征参数，例如检测阈值和几何关系，从而实现对象部分信息配置的无监督发现。我们在广泛的课程中测试了我们的方法并取得了良好的效果。我们还演示了如何使用该方法来无监督地分离和学习单个类别的视觉上相似的子类，例如面部视图或手势。我们使用该方法来比较特征一致性的各种标准，包括互信息、可疑重合、L2 和杰卡德指数。最后，我们将我们的方法与 pLSA 等参数一致性优化技术进行比较，并显示出明显更好的性能。
Class learning, both supervised and unsupervised, requires feature selection, which includes two main components. The first is the selection of a discriminative subset of features from a larger pool. The second is the selection of detection parameters for each feature to optimize classification performance. In this paper we present a method for the discovery of multiple classification features, their detection parameters and their consistent configurations, in the fully unsupervised setting. This is achieved by a global optimization of joint consistency between the features as a function of the detection parameters, without assuming any prior parametric model. We demonstrate how the proposed framework can be applied for learning different types of feature parameters, such as detection thresholds and geometric relations, resulting in the unsupervised discovery of informative configurations of objects parts. We test our approach on a wide range of classes and show good results. We also demonstrate how the approach can be used to unsupervisedly separate and learn visually similar sub-classes of a single category, such as facial views or hand poses. We use the approach to compare various criteria for feature consistency, including Mutual Information, Suspicious Coincidence, L2 and Jaccard index. Finally, we compare our approach to aparametric consistency optimization technique such as pLSA and show significantly better performance.