Maximum Likelihood Estimation Gradient Algorithm and Fast ICA Algorithm

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Resource Overview

This implementation includes both maximum likelihood estimation gradient algorithm and Fast ICA algorithm, which have been successfully debugged. Special attention should be paid to whether the input signal x is provided as a row vector or column vector during implementation and debugging.

Detailed Documentation

This article presents an algorithm implementation containing two distinct methods: the maximum likelihood estimation gradient algorithm and the Fast ICA algorithm. Both methods have been thoroughly tested and successfully debugged. During implementation and debugging, particular attention must be paid to the orientation of the input signal x - whether it is provided as a row vector or column vector, as this affects matrix operations and dimension compatibility in the code. The gradient algorithm for maximum likelihood estimation involves iterative parameter updates using derivative calculations, while Fast ICA employs fixed-point iteration for independent component analysis with optimization for convergence speed and separation performance.

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