We explore the potential of Tensor-Train (TT) decompositions in the context of multi-feature face or object recognition strategies. We devise a new recognition algorithm that can handle three or more way tensors in the TT format, and propose a truncation strategy to limit memory usage. Numerical comparisons with other related methods—including the well established recognition algorithm based on high-order SVD—illustrate the features of the various strategies on benchmark datasets.

中文翻译：

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Tensor-Train分解用于图像识别

我们探索在多特征人脸或物体识别策略的背景下Tensor-Train（TT）分解的潜力。我们设计了一种新的识别算法，该算法可以处理TT格式的三个或更多方向张量，并提出了一种截断策略来限制内存使用。与其他相关方法（包括基于高阶SVD的公认的识别算法）的数值比较说明了基准数据集上各种策略的特征。