Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 22, Issue 9, Pages 1042-1049Publisher
IEEE COMPUTER SOC
DOI: 10.1109/34.877525
Keywords
eigenspace models; principal component analysis; model merging; model splitting; Gaussian mixture models
Ask authors/readers for more resources
We present new deterministic methods that given two eigenspace models-each representing a set of n-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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available