In pattern recognition, when data has different structures in different parts of the
input space, fitting one global model can be slow and inaccurate. Learning methods
can quickly learn the structure of the data in local regions, consequently, offering faster
and more accurate model fitting. Breaking training data set into smaller subsets may
lead to curse of dimensionality problem, as a training sample subset may not be enough
for estimating the required set of parameters for the submodels. Increasing the size of
training data may not be at hand in many situations. Interestingly, the data in local
regions becomes more correlated. Therefore, by decorrelation methods we can reduce
data dimensions and hence the number of parameters. In other words, we can find
uncorrelated low dimensional subspaces that capture most of the data variability. The
current subspace modelling methods have proved better performance than the global
modelling methods for the given type of training data structure. Nevertheless these
methods still need more research work as they are suffering from two limitations
2 There is no standard method to specify the optimal number of subspaces.
² / There is no standard method to specify the optimal dimensionality for each
subspace.
In the current models these two parameters are determined beforehand. In this dissertation
we propose and test algorithms that try to find a suboptimal number of
principal subspaces and a suboptimal dimensionality for each principal subspaces automatically.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/1104512/index.pdf |
| Date | 01 October 2003 |
| Creators | Musa, Mohamed Elhafiz Mustafa |
| Contributors | Atalay, Volkan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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