Multiresolution Kernels
Multiresolution Kernels
复制标题
多分辨率内核
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
K. Fukumizu
中科院分区：
文献类型：
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作者：
Marco Cuturi;K. Fukumizu
在这项工作中，我们提出了一种新的方法来设计内核的数据结构与较小的组件，如文本，图像或序列。这种方法是一个模板程序，可以应用于大多数内核的措施，并利用更详细的“袋组件”表示的对象。为了获得这样一个详细的描述，我们考虑可能的分解成一个集合的嵌套袋，根据先验知识的对象的结构。然后，我们考虑这些较小的袋子来比较两个物体，既可以从详细的角度来比较，强调较小袋子之间的局部匹配，也可以从全局或粗略的角度来比较，考虑整个袋子。这种多分辨率方法可能最适合粗糙方法不够精确的任务，并且需要更微妙的局部和全局相似性的混合来比较对象。这里提出的方法将不会是计算上容易处理的，没有一个因子分解的技巧，我们介绍之前提出的有希望的结果图像检索任务。
We present in this work a new methodology to design kernels on data which is structured with smaller components, such as text, images or sequences. This methodology is a template procedure which can be applied on most kernels on measures and takes advantage of a more detailed “bag of components” representation of the objects. To obtain such a detailed description, we consider possible decompositions of the original bag into a collection of nested bags, following a prior knowledge on the objects’ structure. We then consider these smaller bags to compare two objects both in a detailed perspective, stressing local matches between the smaller bags, and in a global or coarse perspective, by considering the entire bag. This multiresolution approach is likely to be best suited for tasks where the coarse approach is not precise enough, and where a more subtle mixture of both local and global similarities is necessary to compare objects. The approach presented here would not be computationally tractable without a factorization trick that we introduce before presenting promising results on an image retrieval task.