-
1
-
-
0033421955
-
A characterization of principal components for projection pursuit
-
Bolton R.J. and Krzanowski W.J. 1999. A characterization of principal components for projection pursuit. The American Statistician 53(2): 108-109.
-
(1999)
The American Statistician
, vol.53
, Issue.2
, pp. 108-109
-
-
Bolton, R.J.1
Krzanowski, W.J.2
-
3
-
-
0347463354
-
Calibrate your eyes to recognise high-dimensional shapes from their low-dimensional projections
-
Cook D. 1997. Calibrate your eyes to recognise high-dimensional shapes from their low-dimensional projections. Journal of Statistical Software 2(6). www.stat.ucla.edu/journals/jss.
-
(1997)
Journal of Statistical Software
, vol.2
, Issue.6
-
-
Cook, D.1
-
5
-
-
0043015539
-
Nonlinear principal component analysis - Based on principal curves and neural networks
-
Dong D. and McAvoy T.J. 1996. Nonlinear principal component analysis-Based on principal curves and neural networks. Computers and Chemical Engineering 20(1): 65-78.
-
(1996)
Computers and Chemical Engineering
, vol.20
, Issue.1
, pp. 65-78
-
-
Dong, D.1
McAvoy, T.J.2
-
8
-
-
0016102310
-
A projection pursuit algorithm for exploratory data analysis
-
Friedman J.H. and Tukey J.W. 1974. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers C-23: 881-889.
-
(1974)
IEEE Transactions on Computers
, vol.C-23
, pp. 881-889
-
-
Friedman, J.H.1
Tukey, J.W.2
-
10
-
-
0000840529
-
Bayesian radial basis functions of variable dimension
-
Holmes C.C. and Mallick B.K. 1998. Bayesian radial basis functions of variable dimension. Neural Computation 10: 1217-1233.
-
(1998)
Neural Computation
, vol.10
, pp. 1217-1233
-
-
Holmes, C.C.1
Mallick, B.K.2
-
11
-
-
0000263797
-
Projection pursuit
-
Huber P.J. 1985. Projection pursuit (with discussion). The Annals of Statistics 13: 435-525.
-
(1985)
The Annals of Statistics
, vol.13
, pp. 435-525
-
-
Huber, P.J.1
-
13
-
-
0026113980
-
Nonlinear principal component analysis using autoassociative neural networks
-
Kramer M.A. 1991. Nonlinear principal component analysis using autoassociative neural networks. American Institute of Chemical Engineers Journal 37(2): 233-243.
-
(1991)
American Institute of Chemical Engineers Journal
, vol.37
, Issue.2
, pp. 233-243
-
-
Kramer, M.A.1
-
14
-
-
0029221732
-
On the use of nonlocal and non positive definite basis functions in radial basis function networks
-
Cambridge, IEE Conference Publication 409
-
Lowe D. 1995. On the use of nonlocal and non positive definite basis functions in radial basis function networks. In Fourth IEE International Conference on Artificial Neural Networks, Cambridge, pp. 206-211. IEE Conference Publication 409.
-
(1995)
Fourth IEE International Conference on Artificial Neural Networks
, pp. 206-211
-
-
Lowe, D.1
-
15
-
-
0031646493
-
Limitations of nonlinear PCA as performed with generic neural networks
-
Malthouse E.C. 1998. Limitations of nonlinear PCA as performed with generic neural networks. IEEE Transactions on Neural Networks 9(1): 165-173.
-
(1998)
IEEE Transactions on Neural Networks
, vol.9
, Issue.1
, pp. 165-173
-
-
Malthouse, E.C.1
-
16
-
-
0001906155
-
Tools for two-dimensional exploratory projection pursuit
-
Posse C. 1995. Tools for two-dimensional exploratory projection pursuit. Journal of Computational and Graphical Statistics 4(2): 83-100.
-
(1995)
Journal of Computational and Graphical Statistics
, vol.4
, Issue.2
, pp. 83-100
-
-
Posse, C.1
-
17
-
-
0032594954
-
Input space vs. feature space in kernel-based methods
-
Schölkopf B., Mika S., Burges C.J.C., Knirsch P., Müller K.-R., Rätsch G., and Smola A. 1999. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks 10(5): 1000-1017.
-
(1999)
IEEE Transactions on Neural Networks
, vol.10
, Issue.5
, pp. 1000-1017
-
-
Schölkopf, B.1
Mika, S.2
Burges, C.J.C.3
Knirsch, P.4
Müller, K.-R.5
Rätsch, G.6
Smola, A.7
-
18
-
-
21344434075
-
An approach to non-linear principal components analysis using radially symmetric kernel functions
-
Webb A.R. 1996. An approach to non-linear principal components analysis using radially symmetric kernel functions. Statistics and Computing 6: 159-168.
-
(1996)
Statistics and Computing
, vol.6
, pp. 159-168
-
-
Webb, A.R.1
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