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Sparse Alignment for Robust Tensor Learning

机译：稀疏对齐以实现可靠的张量学习

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摘要

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, $L_{1}$ - and $L_{2}$ -norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.
机译：基于流形学习的算法的多线性/张量扩展已广泛用于计算机视觉和模式识别。本文首先通过使用对齐技术对最流行方法的多线性扩展进行系统分析，从而获得通用的张量对齐框架。从这个框架中，很容易表明基于流形学习的张量学习方法本质上不同于对齐技术。基于对齐框架，然后提出了一种鲁棒的张量学习方法，称为稀疏张量对齐（STA），用于无监督张量特征提取。与现有的张量学习方法不同， $ L_ {1} $ -和 $ L_ {2} $ -范数，以增强STA对齐步骤中的鲁棒性。所提出的技术的优点在于，在基于流形学习的张量特征提取算法中，可以避免选择局部邻域大小的困难。尽管STA是一种无监督的学习方法，但稀疏性会在对齐步骤中对区分性信息进行编码，并提供STA的鲁棒性。通过将对象图像编码为张量，在著名的图像数据库以及动作和手势数据库上进行的大量实验表明，与基于张量的无监督学习方法相比，所提出的STA算法具有最具竞争力的性能。

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