Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis

The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data. With multi-components in the mixture, this model can be seen as a soft cluster algorithm and has capability of modeling data with complex structures. A Bayesian inference scheme has been proposed based on the variational EM (Expectation-Maximization) approach for learning model parameters. Experiments on some publicly available databases show that the performance of mixB2DPPCA has been largely improved, resulting in more accurate reconstruction errors and recognition rates than the existing PCA-based algorithms

Ensemble Joint Sparse Low Rank Matrix Decomposition for Thermography Diagnosis System

Composite is widely used in the aircraft industry and it is essential for manufacturers to monitor its health and quality. The most commonly found defects of composite are debonds and delamination. Different inner defects with complex irregular shape is difficult to be diagnosed by using conventional thermal imaging methods. In this paper, an ensemble joint sparse low rank matrix decomposition (EJSLRMD) algorithm is proposed by applying the optical pulse thermography (OPT) diagnosis system. The proposed algorithm jointly models the low rank and sparse pattern by using concatenated feature space. In particular, the weak defects information can be separated from strong noise and the resolution contrast of the defects has significantly been improved. Ensemble iterative sparse modelling are conducted to further enhance the weak information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted to detect the inner debond on multiple carbon fiber reinforced polymer (CFRP) composites. A comparative analysis is presented with general OPT algorithms. Not withstand above, the proposed model has been evaluated on synthetic data and compared with other low rank and sparse matrix decomposition algorithms

An efficient model order selection for PCA mixture model

This paper proposes a fast and sub-optimal selection method of model order such as the number of mixture components and the number of PCA bases for the PCA mixture model, consisting of a combination of many PCAs. Once the model order is determined, the parameters of the model can be easily estimated by the expectation maximization (EM) learning using the decorrelatedness of feature data in the PCA transformed space. The conventional model order selection method takes a long processing time because it requires to perform the time-consuming EM learning over all possible model orders. We try to simplify the model order selection method as follows. First, the time-consuming EM learning over the training data set has been performed once for a given number of mixture components, with all PCA bases kept. Second, in virtue of ordering property of PCA bases, the evaluation step to measure the fitness of model selection criterion over the validation data set has been performed sequentially by pruning less significant PCA base one by one, starting from the most insignificant PCA base. A pair of the number of mixture components and PCA bases that satisfies the model selection criterion fully is selected as the optimal model order for the given problem. Simulation results of the synthetic data classification and a practical problem of alphabet recognition show that the proposed model selection method determines the model order appropriately and improves the classification and detection performances. (C) 2002 Elsevier Science B.V. All rights reserved.X1119sciescopu

An efficient model order selection for PCA mixture model

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