A1898
Title: Contrastive latent functional model
Authors: Damla Senturk - University of California Los Angeles (United States) [presenting]
Yanlong Bai - UCLA (United States)
Danh Nguyen - University of California, Irvine (United States)
Donatello Telesca - UCLA (United States)
Esra Kurum - University of California, Riverside (United States)
Abigail Dickinson - UCLA (United States)
Shafali Jeste - UCLA (United States)
Abstract: Functional Principal Component Analysis (FPCA) is a popular tool for representing Functional data in lower dimensions along highly interpretable components depicting directions of variation in the data across time. The Contrastive Latent Functional Model (cLFM) extends FPCA to Contrastive settings involving multiple groups with a focus on time-dynamic components that are shared across samples versus those that are unique to a particular sample. The proposed Model integrates FPCA with Contrastive learning principles to capture common directions of variation across datasets while isolating distinct features specific to each sample. The Model employs a computationally efficient Expectation-Maximization (EM) algorithm for parameter estimation, ensuring orthogonality between shared and unique Functional spaces. Simulation studies demonstrate the efficacy of the proposed method across different scenarios of shared and unique variation, varying sample size and error variance. Applied to neurodevelopmental and clinical datasets, including EEG studies and longitudinal studies of kidney function, cLFM reveals novel insights into group differences between autism versus neurotypical development and among mild to severe albuminuria subgroups of chronic kidney disease patients. By bridging Contrastive Analysis with Functional data, this framework advances the capacity to disentangle complex variation patterns in multi-group Functional studies in biomedical research.
