Common and Discriminative Subspace Kernel-Based Multiblock Tensor Partial Least Squares Regression
Common and Discriminative Subspace Kernel-Based Multiblock Tensor Partial Least Squares Regression
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DOI：
10.1609/aaai.v30i1.10214
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Ming Hou;Qibin Zhao;B. Chaib-draa;A. Cichocki
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文献类型：
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作者：
Ming Hou;Qibin Zhao;B. Chaib-draa;A. Cichocki
在这项工作中，我们引入了一个新的广义非线性张量回归框架，称为基于核的多块张量偏最小二乘法（KMTPLS），通过提取少量的共同和歧视性潜在成分，从一组独立的张量块预测一组相关的张量块。KMTPLS通过考虑共同特征和区分特征，有效地融合了来自多个张量数据源的信息，并将单块和多块张量回归场景统一到一个通用模型中。此外，与多线性模型相比，KMTPLS通过将核机器与联合Tucker分解相结合，成功地解决了多个响应和预测张量块之间的非线性依赖关系，从而在可预测性方面获得了显着的性能增益。提出了一种基于连续提取公共特征向量和判别特征向量的KMTPLS学习算法。最后，为了展示我们方法的有效性和优势，我们在计算机视觉中的真实回归任务上进行了测试，即，从多视点视频序列重建人体姿态。
In this work, we introduce a new generalized nonlinear tensor regression framework called kernel-based multiblock tensor partial least squares (KMTPLS) for predicting a set of dependent tensor blocks from a set of independent tensor blocks through the extraction of a small number of common and discriminative latent components. By considering both common and discriminative features, KMTPLS effectively fuses the information from multiple tensorial data sources and unifies the single and multiblock tensor regression scenarios into one general model. Moreover, in contrast to multilinear model, KMTPLS successfully addresses the nonlinear dependencies between multiple response and predictor tensor blocks by combining kernel machines with joint Tucker decomposition, resulting in a significant performance gain in terms of predictability. An efficient learning algorithm for KMTPLS based on sequentially extracting common and discriminative latent vectors is also presented. Finally, to show the effectiveness and advantages of our approach, we test it on the real-life regression task in computer vision, i.e., reconstruction of human pose from multiview video sequences.