Many applications, e.g., Web service composition, complex system design, team formation, etc., rely on methods for identifying collections of objects or entities satisfying some functional requirement. Among the collections that satisfy the functional requirement, it is often necessary to identify one or more collections that are optimal with respect to user preferences over a set of attributes that describe the non-functional properties of the collection.
We develop a formalism that lets users express the relative importance among attributes and qualitative preferences over the valuations of each attribute. We define a dominance relation that allows us to compare collections of objects in terms of preferences over attributes of the objects that make up the collection. We establish some key properties of the dominance relation. In particular, we show that the dominance relation is a strict partial order when the intra-attribute preference relations are strict partial orders and the relative importance preference relation is an interval order.
We provide algorithms that use this dominance relation to identify the set of most preferred collections. We show that under certain conditions, the algorithms are guaranteed to return only (sound), all (complete), or at least one (weakly complete) of the most preferred collections. We present results of simulation experiments comparing the proposed algorithms with respect to (a) the quality of solutions (number of most preferred solutions) produced by the algorithms, and (b) their performance and efficiency. We also explore some interesting conjectures suggested by the results of our experiments that relate the properties of the user preferences, the dominance relation, and the algorithms.
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Tensor Vision Technologies（张量视觉技术公司）
视觉对象是由一个递归层次的知觉整体和部分，其属性，如形状，反射率和颜色，构成了一个层次的内在因果因素的对象外观。然而，物体外观是物体的内在和外在因果因素的组成结果，其中外在因果因素与照明和成像条件有关。因此，本文提出了一个统一的张量模型的整体和部分，并介绍了一个组成层次张量分解，解开层次因果结构的对象图像形成，并纳入多线性块张量分解作为一个特殊情况。所得到的对象表示是整体和部分表示的可解释的组合选择，其使得对象识别对遮挡鲁棒并且减少训练数据要求。我们通过在一个极其简化的合成图像数据集上进行训练，在人脸识别的背景下演示了我们的方法，并在两个数据集上报告了人脸验证结果-Freiburg数据集和由真实世界图像组成的Labeled Face in the Wild（LFW）数据集，从而证实了我们的方法适用于数据匮乏的领域。
