【摘 要】
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With the advance of data acquisition techniques, tensor (multi-dimensional data) ob jects are increasingly accumulated and generated, for example, multi-cha
【机 构】
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HongKongBaptistUniversity
【出 处】
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2016年张量和矩阵学术研讨会(International conference on Tensor, Matrix a
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With the advance of data acquisition techniques, tensor (multi-dimensional data) ob jects are increasingly accumulated and generated, for example, multi-channel signals, and multi-view images and videos. In this talk, we propose a new approach to perform dimension reduction for nonnegative tensor ob jects. Our idea is to employ manifold reg-ularization and Tucker decomposition. Extensive experimental results will be provided to demonstrate the effectiveness of the proposed idea.
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