Algorithms for Nonnegative Independent Component Analysis  

Oleh:

Plumbley, M. D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 3 (May 2003), page 534-543.
Topik: algorithms; task components; algorithms; non negative; independent; component analysis

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We consider the task of solving the independent component analysis (ICA) problem x = As given observations x, with a constraint of nonnegativity of the source random vector s. We refer to this as nonnegative independent component analysis and we consider methods for solving this task. For independent sources with nonzero probability density function (pdf) p(s) down to s = 0 it is sufficient to find the orthonormal rotation y = Wz of prewhitened sources z = Vx, which minimizes the mean squared error of the reconstruction of z from the rectified version y+ of y. We suggest some algorithms which perform this, both based on a nonlinear principal component analysis (PCA) approach and on a geodesic search method driven by differential geometry considerations. We demonstrate the operation of these algorithms on an image separation problem, which shows in particular the fast convergence of the rotation and geodesic methods and apply the approach to a musical audio analysis task.

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