A hybrid principal-vector pruning framework refines Koopman subspace invariance with error bounds and rank-one update efficiency for lifted linear prediction.
Note that kernel EDMD performs the orthogonal projection using the kernel inner product, which is different from the standard L2(µX)inner product used in our other examples
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Subspace Pruning via Principal Vectors for Accurate Koopman-Based Approximations
A hybrid principal-vector pruning framework refines Koopman subspace invariance with error bounds and rank-one update efficiency for lifted linear prediction.