SVD decompose
[U,S,V] = svd(I)
当修改主成份U值的时候,这里通过U递增,重新生成图像,观察与原图的区别。
定义:Frobenius范数 ||M - B||来衡量B对M的近似效果。
这意味着,如果我们以Frobenius范数作为衡量准则,那么在给定的矩阵秩的情况下,SVD能够给出最佳的近似效果。。
真实出人意料!
SVD decompose
[U,S,V] = svd(I)
当修改主成份U值的时候,这里通过U递增,重新生成图像,观察与原图的区别。
定义:Frobenius范数 ||M - B||来衡量B对M的近似效果。
这意味着,如果我们以Frobenius范数作为衡量准则,那么在给定的矩阵秩的情况下,SVD能够给出最佳的近似效果。。
真实出人意料!
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