Rapid and Brief CommunicationSubpattern-based principle component analysis
Introduction
The traditional PCA [1] is a very effective approach of extracting features and has successfully been applied in pattern recognition such as face classification [2]. It operates directly on whole patterns represented as (feature) vectors to extract so-needed global features for subsequent classification by a set of previously found global projectors from a given training pattern set, whose aim is to maximally preserve original pattern information after extracting features, i.e., reducing dimensionality. In this paper, we develop another PCA operating directly on subpatterns rather than on whole pattern. These subpatterns are formed via a partition for an original whole pattern and utilized to compose multiple training subpattern sets for the original training pattern set. In this way, SpPCA can independently be performed on individual training subpattern sets and finds corresponding local projection sub-vectors, and then uses them to extract local sub-features from any given pattern. Afterwards, these extracted sub-features from individual subpatterns are synthesized into a global feature of the original whole pattern for subsequent classification.
Section snippets
Proposed SpPCA
SpPCA includes two steps. In the first step, an original whole pattern denoted by a vector is partitioned into a set of equally-sized subpatterns in non-overlapping ways and then all those subpatterns sharing the same original feature components are respectively collected from the training set to compose corresponding training subpattern sets. Secondly, PCA is performed on each of such subpattern sets. More specifically, we are given a set of training patterns X={X1,X2,…,XN} with each column
Experimental results
The experimental datasets are from publicly attainable 8 benchmark datasets including a derived one. For producing as many partitions as possible, all dimensions of patterns are not less than 12. Here we just give brief experimental conditions because of space limited.
(1) ORL face data1 (40 classes, 10 image patterns each class, 5 patterns each class for training and the rest for testing); (2) Letter data2
Conclusions
We proposed a powerful new approach of extracting features based on PCA and pattern partitioning technique, and made PCA become a special case of the proposed SpPCA. By extracting local sub-features from partitioned subpattern sets and then synthesizing them into global features for subsequent classification. As a result, classification accuracies incorporating the NN rule on all datasets employed here are greatly improved.
Acknowledgements
We thank National Science Foundations of China and of Jiangsu under Grant Nos. 60271017 and BK2002092, the “QingLan” Project Foundation of Jiangsu Province and the Returnee Foundation of China Scholarship Council for partial supports respectively.
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