Unsupervised neural network learning for blind sources separation

Review of independent component analyses (ICA) and blind sources separation (BSS) employing in terms of unsupervised neural networks technology are given. For example, imagery features occurring in human visual systems are the continuing reduction of redundancy towards the “sparse edge maps”. When edges are multiplying together as the vector inner product they result in almost zero, namely pseudo-orthogonal ICA. This fact has been derived from the first principle of artificial neural networks using the maximum entropy information-theoretical formalism by Bell and Sejnowski (1996). We explore the blind de-mixing condition for more than two objects using two sensor measurement. We design two smart cameras with short term working memory to do better image de-mixing of more than two objects. We consider channel communication application that we can efficiently mix four images using matrices [A₀] and [A₁] to send through two channels