Optimal transforms of random vectors: The case of successive optimizations

We propose and justify new transforms of random vectors which provide, under a certain condition, better associated accuracy than that of the optimal transforms, the generic Karhunen-Loève transform and the transform considered by Brillinger. It is achieved by special structures of the proposed transforms which contain more parameters to optimize compared to the known transforms.