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Common and Discriminative Subspace Kernel-Based Multiblock Tensor Partial Least Squares Regression

AAAI 2016 · Learning theory and algorithms › Kernels and regression theory · approach: RKHS kernel methods · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
fuse left and right cameras (2 blocks) instead of left camera aloneKMTPLS · UMPM TableQ: 0.5995 -> 0.6847Helped
fuse left and right cameras (2 blocks) instead of right camera aloneKMTPLS · UMPM TableQ: 0.5906 -> 0.6847Helped
KMTPLS instead of MMCR with front and right camerasKMTPLS · UMPM GrabQ: 0.6111 -> 0.7950Helped
KMTPLS instead of MMCR with front and right camerasKMTPLS · UMPM GrabRMSEP: 351.2 -> 172.8Helped
KMTPLS instead of MMCR with left and right camerasKMTPLS · UMPM TableQ: 0.6761 -> 0.6847Helped
KMTPLS instead of MMCR with front and right camerasKMTPLS · UMPM TableRMSEP: 277.2 -> 291.2Hurt
KMTPLS instead of MMCR with 4 blocksKMTPLS · MHAD S6 BendingQ: 0.6630 -> 0.7129Helped
KMTPLS instead of MMCR with 4 blocksKMTPLS · MHAD S6 Jumping in the PlaceQ: 0.6790 -> 0.7000Helped
KMTPLS instead of MMCR with 4 blocks, subject S6 bendingKMTPLS · MHAD S6 BendingRMSEP: 210.8 -> 179.0Helped
KMTPLS instead of MMCR with 4 blocksKMTPLS · MHAD S6 Jumping in the Placelearning time (ms): 2954 -> 139Helped
increase input blocks from 1 to 4KMTPLS · MHAD S6 Jumping in the PlaceQ: 0.6836 -> 0.7000Helped
increase input blocks from 1 to 4KMTPLS · MHAD S12 Jumping in the PlaceQ: 0.7059 -> 0.7265Helped

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