DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages

31 Jul 2014Lifang HeXiangnan KongPhilip S. YuAnn B. RaginZhifeng HaoXiaowei Yang

With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost... (read more)

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