Local Learning Multiple Probabilistic Linear Discriminant Analysis
Probabilistic Linear Discriminant Analysis (PLDA) has delivered impressive results in some challenging tasks, e.g. face recognition and speaker recognition. Similar with the most state-of-the-art machine learning techniques, PLDA tries to globally learn the model parameters over the whole training set. However, those globally-learnt PLDA parameters can hardly characterize all relevant information, especially for those data sets whose underlying feature-spaces are heterogeneous and abound in complex manifolds. PLDA has the data homogeneous assumption which could be interpreted by involved parameters estimated through the entire training dataset. Such a global learning idea has been proven ineffective in the case of the heterogeneous data. In this paper, we alleviate this assumption by separating the feature space and locally learning multiple PLDA models of each space. Various standard datasets are performed and the superiority of the proposed method over the original PLDA could be found. We complete this work by assigning a probability to measure which models the test individual data match. This probabilistic scoring approach could further integrate different recognition technologies including other kinds of biological characteristics recognition. We propose the novel log likelihood score in recognition part includes three steps to complete.
KeywordsLocal learning Probabilistic linear discriminant analysis Clustering Bayesian method Fusion
Thanks to NSFC (61105017) agency for funding.
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