A0417
Title: D-CDLF: Decomposition of common and distinctive latent factors for multi-view high-dimensional data
Authors: Hai Shu - New York University (United States) [presenting]
Abstract: Modern biomedical studies often collect multi-view data, that is, multiple types of data measured on the same set of objects. A typical approach to the joint analysis of multiple high-dimensional data views is to decompose each view's data matrix into three parts: A low-rank common-source matrix generated by common latent factors of all data views, a low-rank distinctive-source matrix generated by distinctive latent factors of the corresponding data view, and an additive noise matrix. Existing decomposition methods often focus on the uncorrelatedness between the common latent factors and distinctive latent factors but inadequately address the equally necessary uncorrelatedness between distinctive latent factors from different data views. A novel decomposition method is proposed, called decomposition of common and distinctive latent factors (D-CDLF), to effectively achieve both types of uncorrelatedness. Consistent estimators of the D-CDLF method are established with good finite-sample numerical performance. The superiority of D-CDLF over state-of-the-art methods is also corroborated in simulations and real-world data analysis.
