13 citations · 43 across the 10 of their papers we have counts for
6 papers · 1 filter
Dependence Induced Representations
Xiangxiang Xu, Lizhong Zheng
We study the problem of learning feature representations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide suf…
Operator SVD with Neural Networks via Nested Low-Rank Approximation
J. Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol +3
Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scien…
Neural Feature Learning in Function Space
Xiangxiang Xu, Lizhong Zheng
We present a novel framework for learning system design with neural feature extractors. First, we introduce the feature geometry, which unifies statistical dependence and feature r…
Kernel Subspace and Feature Extraction
Xiangxiang Xu, Lizhong Zheng
We study kernel methods in machine learning from the perspective of feature subspace. We establish a one-to-one correspondence between feature subspaces and kernels and propose an…
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
Fei Ma, Xiangxiang Xu, Shao-Lun Huang +1
Multimodal learning has achieved great successes in many scenarios. Compared with unimodal learning, it can effectively combine the information from different modalities to improve…
An Efficient Approach to Informative Feature Extraction from Multimodal Data
Lichen Wang, Jiaxiang Wu, Shao-Lun Huang +4
One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the…