Abstract
The non-negative ICA problem is here defined by the constraint that the sources are non-negative with probability one. This case occurs in many practical applications like spectral or image analysis. It has then been shown by [10] that there is a straightforward way to find the sources: if one whitens the non-zero-mean observations and makes a rotation to positive factors, then these must be the original sources. A fast algorithm, resembling the FastICA method, is suggested here, rigorously analyzed, and experimented with in a simple image separation example.
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Yuan, Z., Oja, E. (2004). A FastICA Algorithm for Non-negative Independent Component Analysis. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_1
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DOI: https://doi.org/10.1007/978-3-540-30110-3_1
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