Locality Preserving Discriminative Complex-Valued Latent Variable Model

Techniques for analyzing complex-valued data are required in numerous fields, such as signal processing. This work develops a novel complex-valued latent variable model, named locality-preserving discriminative complex-valued Gaussian process latent variable model (LPD-CGPLVM), for discovering a compressed complex-valued representation of data. The developed LPD-CGPLVM operates on the complex-valued domain. Additionally, we attempt to preserve both global and local data structures while promoting discrimination. A new objective function that imposes a locality-preserving and a discriminative term for complex-valued data is presented. Complex-valued gradient descent is then utilized to obtain a complex-valued representation of high-dimensional data and the hyperparameters in the LPD-CGPLVM. The proposed method was evaluated using two pattern recognition applications - face recognition with occlusion and music emotion recognition. The experimental results thus obtained demonstrated the superior accuracy of the proposed method, especially for situations with only a small number of training data.