A "Nonnegative PCA" Algorithm for Independent Component Analysis  

Oleh:

Oja, E. ; Plumbley, M. D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 1 (Jan. 2004), page 66-76.
Topik: algorithms; non negative PCA; algorithm; independent component analysis

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We consider the task of independent component analysis when the independent sources are known to be nonnegative and well - grounded, so that they have a non zero probability density function (pdf) in the region of zero. We propose the use of a "nonnegative principal component analysis (nonnegative PCA)" algorithm, which is a special case of the non linear PCA algorithm, but with a rectification non linearity, and we conjecture that this algorithm will find such non negative well - grounded independent sources, under reasonable initial conditions. While the algorithm has proved difficult to analyze in the general case, we give some analytical results that are consistent with this conjecture and some numerical simulations that illustrate its operation.

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