 |
Both the signal flow graph in Figure 5 and the deep network model in Figure 4 are iterative algorithms.
They are considered to be similar, with 〖𝒘_𝑚^H 𝒍[𝑘] corresponding to the linear layer and the symmetric orthogonalization and fast immovable point algorithms corresponding to the activation function layer.
By optimizing the learnable parameters introduced in the fast fixed point algorithm, we improve the accuracy of ICA for short data series lengths.
The evolution structure of the iterative process of ICA with learnable parameters 𝛼 can be regarded as an FFNN.
However, the learnable parameters are adjusted by applying standard deep learning techniques (error back propagation, stochastic gradient method).
This approach of treating an iterative algorithm as an FFNN by expanding it is called deep expansion.
|
IEEE/ICACT20230070 Slide.16
[Big Slide]