基于平方损失互的依赖最大化时间对准 信息

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摘要翻译：
时间比对的目标是建立两个序列之间的时间对应关系，它在语音处理、生物信息学、计算机视觉、计算机图形学等领域有着广泛的应用。本文提出了一种新的时间对准方法，称为最小二乘动态时间规整(LSDTW)。LSDTW通过互信息的平方损失变量来度量，找到一种最大限度地提高序列之间统计相关性的对齐方式。这种新的信息论公式的好处是，LSDTW可以以计算效率高的方式对不同长度、不同维数、高度非线性和非高斯的序列进行对齐。此外，模型参数如初始对准矩阵可以通过交叉验证系统地优化。我们通过在合成和真实世界的Kinect动作识别数据集上的实验证明了LSDTW的有效性。
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英文标题：
《Dependence Maximizing Temporal Alignment via Squared-Loss Mutual
  Information》
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作者：
Makoto Yamada, Leonid Sigal, Michalis Raptis, Masashi Sugiyama
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最新提交年份：
2012
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW). LSDTW finds an alignment that maximizes statistical dependency between sequences, measured by a squared-loss variant of mutual information. The benefit of this novel information-theoretic formulation is that LSDTW can align sequences with different lengths, different dimensionality, high non-linearity, and non-Gaussianity in a computationally efficient manner. In addition, model parameters such as an initial alignment matrix can be systematically optimized by cross-validation. We demonstrate the usefulness of LSDTW through experiments on synthetic and real-world Kinect action recognition datasets.
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PDF链接：
https://arxiv.org/pdf/1206.4116

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关键词：最大化 Intelligence Presentation Applications information 信息学 公式 对齐 识别 different