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lorenz_ext
- m为嵌入空间维数 tau为时间延迟 data为输入时间序列 N为时间序列长度 X为输出,是m*M维矩阵- M for the embedding space dimension tau is the time delay for the input data time series N for the time series length of X to output, is m* M-dimensional matrix
2012--Improving-the-embedding-efficiency-of-weigh
- Improving the embedding efficiency of weight matrix-based steganography for grayscale images
LGME
- input: param: parameters of the LMGE algorithm param.mu, param.alpha, param.beta are regularization parameters. param.p: dimension of shared subspace param.k: number of nearest neighbors for Laplacian matrix X: input data Y: ground
icml2010-code(2)
- Power Iteration Clustering projection Input: W - row-normalized affinity matrix v0 - starting vector conv - convergence threshold maxit - maximum number of iterations Output: vt - 1-d PIC embedding i - iterations ran