1.7k citations · 5.2k across the 50 of their papers we have counts for
12 papers · 1 filter
Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder
Qi Lyu, Xiao Fu
This work focuses on the problem of unraveling nonlinearly mixed latent components in an unsupervised manner. The latent components are assumed to reside in the probability simplex…
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
Shahana Ibrahim, Xiao Fu
Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably…
Stochastic Block-ADMM for Training Deep Networks
Saeed Khorram, Xiao Fu, Mohamad H. Danesh +2
In this paper, we propose Stochastic Block-ADMM as an approach to train deep neural networks in batch and online settings. Our method works by splitting neural networks into an arb…
StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling
Eugene Seo, Rebecca A. Hutchinson, Xiao Fu +4
This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a lan…
Mixed Membership Graph Clustering via Systematic Edge Query
Shahana Ibrahim, Xiao Fu
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is n…
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikos Kargas +1
The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, no…
Nonlinear Multiview Analysis: Identifiability and Neural Network-assisted Implementation
Qi Lyu, Xiao Fu
Multiview analysis aims at extracting shared latent components from data samples that are acquired in different domains, e.g., image, text, and audio. Classic multiview analysis, e…
Learning Nonlinear Mixtures: Identifiability and Algorithm
Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos +1
Linear mixture models have proven very useful in a plethora of applications, e.g., topic modeling, clustering, and source separation. As a critical aspect of the linear mixture mod…
Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data
Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos +1
The sum-of-correlations (SUMCOR) formulation of generalized canonical correlation analysis (GCCA) seeks highly correlated low-dimensional representations of different views via max…
On Identifiability of Nonnegative Matrix Factorization
Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos
In this letter, we propose a new identification criterion that guarantees the recovery of the low-rank latent factors in the nonnegative matrix factorization (NMF) model, under mil…
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos +1
Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can…
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering
Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos
Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessi…