Robustifying Algorithms of Learning Latent Trees with Vector Variables
Robustifying Algorithms of Learning Latent Trees with Vector Variables
We consider learning the structures of Gaussian latent tree models with vector observations when a subset of them are arbitrarily corrupted. First, we present the sample complexities of Recursive Grouping (RG) and Chow-Liu Recursive Grouping (CLRG) without the assumption that the effective depth is bounded in the number of observed …