Global Convergence of Oja's Subspace Algorithm for Principal Component Extraction  

Oleh:

Chen, Tianping ; Hua, Yingbo ; Yan, Wei-Yong

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 1 (1998), page 58-67.
Topik: extraction; global convergence; oja's subspace; algorithm; principal; ocmponent; extractin

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Oja's principal subspace algorithm is a well - known and powerful technique for learning and tracking principal information in time series. A thorough investigation of the convergence property of Oja's algorithm is undertaken in this paper. The asymptotic convergence rates of the algorithm is discovered. The dependence of the algorithm on its initial weight matrix and the singularity of the data covariance matrix is comprehensively addressed.

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