Beyond smoothness : Incorporating low-rank analysis into nonparametric density estimation

• Main Authors: VANDERMEULEN, Rob, LEDENT, Antoine
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Density estimation; Low-rank methods; Tensor methods; Tucker decomposition; statistical guarantees; bias-variance analysis.; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/7205; https://ink.library.smu.edu.sg/context/sis_research/article/8208/viewcontent/Low_rank.pdf
• Institution: Singapore Management University

The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property: smoothness. In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model,...