Issue No. 09 - September (2000 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.877525
<p><b>Abstract</b>—We present new deterministic methods that given two eigenspace models—each representing a set of <tmath>$n$</tmath>-dimensional observations—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.</p>
Eigenspace models, principal component analysis, model merging, model splitting, Gaussian mixture models.
R. Martin, P. Hall and D. Marshall, "Merging and Splitting Eigenspace Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 1042-1049, 2000.