25 citations · 52 across the 11 of their papers we have counts for
11 papers
Provable Subspace Identification of Nonlinear Multi-view CCA
Zhiwei Han, Stefan Matthes, Hao Shen
We investigate the identifiability of nonlinear canonical correlation analysis (CCA) in a multi-view setup, in which each view is generated by applying an unknown nonlinear map to…
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations
Jianzhang Zheng, Hao Shen, Jian Yang +5
Autoencoders have achieved great success in various computer vision applications. The autoencoder learns appropriate low dimensional image representations through the self-supervis…
Metapath- and Entity-aware Graph Neural Network for Recommendation
Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy +6
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node conn…
Joint Learning of Discriminative Low-dimensional Image Representations Based on Dictionary Learning and Two-layer Orthogonal Projections
Xian Wei, Hao Shen, Yuanxiang Li +4
There are some inadequacies in the language description of this paper that require further improvement. This paper is based on a revision of a conference paper. It is now necessary…
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations
Xian Wei, Hao Shen, Martin Kleinsteuber
This work studies the problem of learning appropriate low dimensional image representations. We propose a generic algorithmic framework, which leverages two classic representation…
Fault-Tolerant Control of Linear Quantum Stochastic Systems
Shi Wang, Daoyi Dong
In quantum engineering, faults may occur in a quantum control system, which will cause the quantum control system unstable or deteriorate other relevant performance of the system.…
Dynamic Variational Autoencoders for Visual Process Modeling
Alexander Sagel, Hao Shen
This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We pro…
An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures
Xian Wei, Hao Shen, Martin Kleinsteuber
Video representation is an important and challenging task in the computer vision community. In this paper, we assume that image frames of a moving scene can be modeled as a Linear…
Averaging Complex Subspaces via a Karcher Mean Approach
Knut Hüper, Martin Kleinsteuber, Hao Shen
We propose a conjugate gradient type optimization technique for the computation of the Karcher mean on the set of complex linear subspaces of fixed dimension, modeled by the so-cal…
Uniqueness Analysis of Non-Unitary Matrix Joint Diagonalization
Martin Kleinsteuber, Hao Shen
Matrix Joint Diagonalization (MJD) is a powerful approach for solving the Blind Source Separation (BSS) problem. It relies on the construction of matrices which are diagonalized by…
Blind Source Separation with Compressively Sensed Linear Mixtures
Martin Kleinsteuber, Hao Shen
This work studies the problem of simultaneously separating and reconstructing signals from compressively sensed linear mixtures. We assume that all source signals share a common sp…