Robust PARAFAC for incomplete data

Bayesian Robust PCA for Incomplete Data

We present a probabilistic model for robust principal component analysis (PCA) in which the observation noise is modelled by Student-t distributions that are independent for different data dimensions. A heavy-tailed noise distribution is used to reduce the negative effect of outliers. Intractability of posterior evaluation is solved using variational Bayesian approximation methods. We show expe...

Image Reconstruction from Incomplete Data

Spectral Compressed Sensing via CANDECOMP/PARAFAC Decomposition of Incomplete Tensors

We consider the line spectral estimation problem which aims to recover a mixture of complex sinusoids from a small number of randomly observed time domain samples. Compressed sensing methods formulates line spectral estimation as a sparse signal recovery problem by discretizing the continuous frequency parameter space into a finite set of grid points. Discretization, however, inevitably incurs ...

Image Reconstruction from Incomplete Data

Robust Bayesian Tensor Factorization for Incomplete Multiway Data

We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The lowCP-rank tensor is ...