LDA-based probabilistic graphical model for excitation-emission matrices

Abstract

In this paper, we present a novel latent Dirichlet allocation (LDA) based probabilistic graphical approach for modeling and analyzing fluorescent spectroscopy excitation-emission Matrices (EEMs). By viewing the EEMs as being generated from an underlying hidden pool of flourophore compounds, the proposed method provides a latent flourophore-space representation of an EEM. We show that this LDA-based model can increase classification performance, especially when paired with parallel factor analysis (PARAFAC) which may be regarded as perhaps the most popular and widely used tool for dealing with EEMs. Our experiments show that the proposed LDA-based algorithm is in some cases more robust than PARAFAC to certain types of noise and data disturbances. We also observe that pairing this LDA-based method with PARAFAC leads to an improvement in classification performance and to added robustness at high peak-signal-to-noise-ratio (PSNR) values.