Khám Phá Cấu Trúc Dữ Liệu Phi Tuyến Tính Sử Dụng Mạng Nơ-ron Hebbian Đơn Giản
Tóm tắt
Từ khóa
#mạng nơ-ron #học Hebbian #phân tích thành phần chính #khám phá chiếu #dữ liệu phi tuyến tínhTài liệu tham khảo
Baldi P, Hornik K (1988) Neural networks and principal component analysis learning from examples without local minima. Neural Networks 2:53–58
DeMers D, Cottrell G (1993) Non-linear dimensionality reduction. ftp site.
Diaconis P, Freedman D (1984) Asymptotics of graphical projections. Ann Stat 12:793–815
Friedman JH (1987) Exploratory projection pursuit. J Am Stat Assoc 82:249–266
Fyfe C (1993a) Interneurons which identify principal components. In:Recent advances in neural networks, bnns 93, Conference of the British Neural Networks Society
Fyfe C (1993b) Pca properties of interneurons. In:From neurobiology to real world computing, icann 93, International Conference on Artificial Neural Networks
Horswell RL, Looney SW (1992) A comparison of tests for multivariate normality that are based on measures of multivariate skewness and kurtosis. J Stat Comput Simulations 42:21–38
Huber PJ (1985) Projection pursuit. Ann Stat 13:435–475
Jones MC, Sibson R (1987) What is projection pursuit. Royal Statistical Society
Karhunen J (1994) Stability of oja's pca subspace rule. Neural Comput (preprint)
Karhunen J, Joutsensalo J (1992) Nonlinear hebbian algorithms for sinusoidal frequency estimation. Aleksander I, Taylor J, (eds) Artificial neural networks 2. North-Holland, Amsterdam, 1099–1103
Karhunen J, Joutsensalo J (1993a) Learning of robust principal component subspace. International Joint Conference on Neural Networks 2409–2412
Karhunen J, Joutsensalo J (1993b) Nonlinear generalizations of principal component learning algorithms. International Joint Conference on Neural Networks 2599–2602
Karhunen J, Joutsensalo J (1994) Representation and separation of signals using nonlinear pca type learning. Neural Networks 7:113–127
Kashyap RL, Blaydon CC, Fu KS (1994) A prelude to neural networks: Adaptive and learning systems. Prentice Hall, New York, 329–355
Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis. Academic Press, London
Oja E (1982) A simplified neuron model as a principal component analyser. J Math Biol 15:267–273
Oja E (1989) Neural networks, principal components and subspaces. Int J Neural Syst. 1:61–68
Oja E, Karhunen J (1993) Nonlinear pca:algorithms and applications (Tech. report A18) University of Technology, Helsinki
Oja E, Ogawa J, Wangviwattana J (1991) Learning in nonlinear constrained hebbian networks. Kohonen T, Makisara K, Simula O, Kangas J (eds) Artificial neural networks. Elsevier, Amsterdam, 385–390
Oja E, Ogawa H, Wangviwattana J (1992a) Pca in fully parallel neural networks. In:Aleksander I, Taylor J (eds) Artificial neural networks, 2. North-Holland, Amsterdam
Oja E, Ogawa H, Wangviwattana J (1992b) Principal component analysis by homogeneous neural networks, part 2: analysis and extensions of the learning algorithms. Ieice Trans Inf. Syst E75-D (3):375–381
Sanger TD (1990) Analysis of the two-dimensional receptive fields learned by the generalised hebbian algorithm in response to andom input. Biol Cybern 63:221–228
Shapiro JL, Prugel-Bennett A (1992) Unsupervised hebbian learning and the shape of the neuron activation function. In: Aleksander I, Taylor J (eds) Artificial neural networks 2. North-Holland, Amsterdam
