Foley-Sammon Optimal Discriminant Vectors Using Kernel Approach  

Oleh:

Zheng, Wenming ; Zhao, Li ; Zou, Cairong

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 1 (Jan. 2005), page 1-9.
Topik: kernel estimator; folley - sammon; disciminant vectors; kernel approach

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A new non linear feature extraction method called kernel Foley - Sammon optimal discriminant vectors (KFSODV s) is presented in this paper. This new method extends the well - known Foley - Sammon optimal discriminant vectors (FSODV s) from linear domain to a nonlinear domain via the kernel trick that has been used in support vector machine (SVM) and other commonly used kernel - based learning algorithms. The proposed method also provides an effective technique to solve the so - called small sample size (SSS) problem which exists in many classification problems such as face recognition. We give the derivation of KFSODV and conduct experiments on both simulated and real data sets to confirm that the KFSODV method is superior to the previous commonly used kernel - based learning algorithms in terms of the performance of discrimination.

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