Fundamentals of digital image processing
Fundamentals of digital image processing
Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
Dimension reduction by local principal component analysis
Neural Computation
Bayesian Ying-Yang machine, clustering and number of clusters
Pattern Recognition Letters - special issue on pattern recognition in practice V
Mixtures of probabilistic principal component analyzers
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Principal Component Analysis of Multispectral Images Using Neural Network
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Principles of Optimal Design
Temporal BYY learning for state space approach, hidden Markovmodel, and blind source separation
IEEE Transactions on Signal Processing
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Rule-based Management of Large Unorganized Data Sets
ITNG '12 Proceedings of the 2012 Ninth International Conference on Information Technology - New Generations
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
BYY harmony learning, independent state space, and generalized APT financial analyses
IEEE Transactions on Neural Networks
An efficient discriminant-based solution for small sample size problem
Pattern Recognition
Sparse neighbor representation for classification
Pattern Recognition Letters
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
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The two traditional tasks of object detection and star/galaxy classification in astronomy can be automated by neural networks because the nature of the problems is that of pattern recognition. A typical existing system can be further improved by using one of the local Principal Component Analysis (PCA) models. Our analysis in the context of object detection and star/galaxy classification reveals that local PCA is not only superior to global PCA in feature extraction, but is also superior to gaussian mixture in clustering analysis. Unlike global PCA which performs PCA for the whole data set, local PCA applies PCA individually to each cluster of data. As a result, local PCA often outperforms global PCA for data of multi-modes. Moreover, since local PCA can effectively avoid the trouble of having to specify a large number of free elements of each covariance matrix of gaussian mixture, it can give a better description of local subspace structures of each cluster when applied on high dimensional data with small sample size. In this paper, the local PCA model proposed by Xu [IEEE Trans. Neural Networks 12 (2001) 822] under the general framework of Bayesian Ying Yang (BYY) normalization learning will be adopted. Endowed with the automatic model selection ability of BYY learning, the BYY normalization learning-based local PCA model can cope with those object detection and star/galaxy classification tasks with unknown model complexity. A detailed algorithm for implementation of the local PCA model will be proposed, and experimental results using both synthetic and real astronomical data will be demonstrated.