Our algorithm is based on the semidefinite program (SDP) relaxation of the non-convexl1-regularized PCA problem.

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Additionally, our approach accelerates learning of walking tasks and increases learning success rate.

The flexibility of these methods is often limited bytherequirement thatthecutsbeaxisaligned.

Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses.

Also, forsimplicity, assume t =1 .