2011/05/09 by Arnau Tibau Puig, Puig, Arnau Tibau, Alfred O. Hero +1
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Mapping and Diversity in Plants and Animals #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1105.1758
openalex publication_date 2011/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in applications where variables are activated in a specific order, which is unknown. The proposed method is based on a linear model that accounts for each factor's inherent delays and relative order. We present an algorithm to fit the model in an unsupervised manner using techniques from convex and non-convex optimization that enforce sparsity of the factor scores and consistent precedence-order of the factor loadings. We illustrate the Order-Preserving Factor Analysis (OPFA) method for the problem of extracting precedence-ordered factors from a longitudinal (time course) study of gene expression data.