Multivariate Adaptive Embedding, MAE-Process
The multivariate adaptive embedding (MAE-Process) provides an adaptive System which creates artificial neural network in the form of an appropriate model of the training-data set by using a globally optimal optimisation and acts in most cases without iterations and parameter settings. There is basically no change to input data and the training-data set is prepared by a special fitting method in order to make it treatable for spectral methods. In the working phase, new input data can be processed multivariatly by the system with good generalising properties. In combination with a Wavelet transformation (WT) for noise- and data reduction, the System performs fast and efficiently in classifying parameterised curves, such as Raman spectra.
KeywordsMachine learning Multi-class classification Cluster identification
Unable to display preview. Download preview PDF.
- 1.Bengio, Y., et al.: Spectral Dimensionality Reduction, Universit de Montreal. Internet (2006), http://www.iro.umontreal.ca/bengioy
- 2.Brand, M.: Minimax embeddings Mitsubishi Electric Research Labs, Cambridge MA 02139 USA. Internet (2006), http://www.merl.com
- 3.Brand, M.: A unifying theorem for spectral embedding and clustering, Mitsubishi Electric Research Labs, Cambridge MA 02139 USA. Internet (2006), http://www.merl.com
- 4.Kokiopoulu, E., Saad, Y.: Orthogonal Neighborhood Preserving Projections, Computer Science and Engineering Department University of Minnesota. Internet (2006), http://lts1pc19.epfl.ch/repository/Kokiopoulou20051351.pdf
- 8.Tsai, F.S., Wu, Y., Chan, K.L.: Nonlinear Dimensionality Reduction Techniques and their Applications. EEE Research Bulletin, S52–S53 (2004)Google Scholar