Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
This is an incremental improvement for researchers in machine learning and data analysis, offering a better method for handling complex data structures in PCA-based algorithms.
The paper tackled the problem of modeling complex data variation by proposing a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) for 2D data, which improved performance with more accurate reconstruction errors and recognition rates compared to existing PCA-based algorithms.
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.