A Neural Network Learning for Adaptively Extracting Cross-Correlation Features Between Two High-Dimensional Data Streams  

Oleh:

Feng, Da-Zheng ; Zhang, Xian-Da ; Bao, Zheng

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 6 (Nov. 2004), page 1541-1554.
Topik: NEURAL NETWORKS; neural network; learning; cross - correlation features; two high - dimensional; data streams

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.11 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

This paper proposes a novel cross - correlation neural network (CNN) model for finding the principal singular subspace of a cross - correlation matrix between two high - dimensional data streams. We introduce a novel non quadratic criterion (NQC) for searching the optimum weights of two linear neural networks (LNN). The NQC exhibits a single global minimum attained if and only if the weight matrices of the left and right neural networks span the left and right principal singular subspace of a cross - correlation matrix, respectively. The other stationary points of the NQC are (unstable) saddle points. We develop an adaptive algorithm based on the NQC for tracking the principal singular subspace of a cross - correlation matrix between two high - dimensional vector sequences. The NQC algorithm provides a fast online learning of the optimum weights for two LNN. The global asymptotic stability of the NQC algorithm is analyzed. The NQC algorithm has several key advantages such as faster convergence, which is illustrated through simulations.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali