Merging and Splitting Eigenspace Models

Authors:
Peter Hall;David Marshall;Ralph Martin
Affiliations:
Univ. of Bath, Bath, UK;Univ. of Wales, Wales, UK;Univ. of Wales, Wales, UK
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2000

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Abstract

We present new deterministic methods that given two eigenspace models驴each representing a set of $n$-dimensional observations驴will: 1) merge the models to yield a representation of the union of the sets and 2) split one model from another to represent the difference between the sets. As this is done, we accurately keep track of the mean. Here, we give a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the construction of Gaussian mixture models that are dynamically updateable.