### Feature PDF

In PDF projection, we assume that the PDF of the features is known. Unless there is a need to identify special hypotheses, it is normally denoted by . In practice, is estimated from available training data, or may be given. If is a fixed-size vector, can be modeled as a Gaussian mixture (Section 13.2). If consists of a sequence of feature vectors, the PDF must be the joint distribution of the entire sequence, typically calculated under a Markov assumption using a hidden Markov model and the forward procedure (Section 13.3).

In practice, the input data can be segmented. In this case, can represent a single segment, or else the entire data record, depending on the application. Important is that we must be consistent, so if represents the entire data record, so must represent the collection of all feature vectors extracted from , and if represents one segment, must be the feature extracted from that segment.

Baggenstoss 2017-05-19