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Complex function of a few features of many inputs

Figure 18: Examples of preprocessing high dimensional data for locally weighted learning

Some types of data are very high dimensional. Examples of this are images, acoustic signals, and time series data. In each case, the data is often pre-processed to extract relevant features (as in fig. 18). Common feature extraction routines include principal components analysis, transformation to the frequency domain, and histogramming. The results of the feature extraction often yield a data set like those shown in section 4.3. Examples of this kind of application are:

Jeff Schneider
Fri Feb 7 18:00:08 EST 1997