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Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses
Wenqiang Pu, Shahana Ibrahim, Xiao Fu +1
This work considers low-rank canonical polyadic decomposition (CPD) under a class of non-Euclidean loss functions that frequently arise in statistical machine learning and signal p…
Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
Shahana Ibrahim, Xiao Fu
Learning the joint probability of random variables (RVs) is the cornerstone of statistical signal processing and machine learning. However, direct nonparametric estimation for high…
On Convergence of Epanechnikov Mean Shift
Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos
Epanechnikov Mean Shift is a simple yet empirically very effective algorithm for clustering. It localizes the centroids of data clusters via estimating modes of the probability dis…
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos
In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i.e., words that only appear…
Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering
Xiao Fu, Kejun Huang, Bo Yang +2
This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF). VolMin is a factorization criterion that decomposes a given data matrix into a…
Tensor Decomposition for Signal Processing and Machine Learning
Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu +3
Tensors or {\em multi-way arrays} are functions of three or more indices -- similar to matrices (two-way arrays), which are functions of two indices for (r…
Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis
Xiao Fu, Kejun Huang, Mingyi Hong +2
Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same ent…
Joint Tensor Factorization and Outlying Slab Suppression with Applications
Xiao Fu, Kejun Huang, Wing-Kin Ma +2
We consider factoring low-rank tensors in the presence of outlying slabs. This problem is important in practice, because data collected in many real-world applications, such as spe…
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches
Xiao Fu, Wing-Kin Ma, José Bioucas-Dias +1
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing. By using an available spectra…
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related
Xiao Fu, Wing-Kin Ma, Tsung-Han Chan +1
This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the m…