Vertical federated learning: Concepts, advances, and challenges
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …
different features about the same set of users jointly train machine learning models without …
Statistical pattern recognition: A review
The primary goal of pattern recognition is supervised or unsupervised classification. Among
the various frameworks in which pattern recognition has been traditionally formulated, the …
the various frameworks in which pattern recognition has been traditionally formulated, the …
Uncertainty-aware multiview deep learning for internet of things applications
As an essential approach in many Internet of Things (IoT) applications, multiview learning
synthesizes multiple features to achieve more comprehensive descriptions of data items …
synthesizes multiple features to achieve more comprehensive descriptions of data items …
A study of graph-based system for multi-view clustering
This paper studies clustering of multi-view data, known as multi-view clustering. Among
existing multi-view clustering methods, one representative category of methods is the graph …
existing multi-view clustering methods, one representative category of methods is the graph …
Detecting coherent groups in crowd scenes by multiview clustering
Detecting coherent groups is fundamentally important for crowd behavior analysis. In the
past few decades, plenty of works have been conducted on this topic, but most of them have …
past few decades, plenty of works have been conducted on this topic, but most of them have …
Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion
With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …
major stream for big data, where each modal/view encodes individual property of data …
Unsupervised adaptive feature selection with binary hashing
Unsupervised feature selection chooses a subset of discriminative features to reduce feature
dimension under the unsupervised learning paradigm. Although lots of efforts have been …
dimension under the unsupervised learning paradigm. Although lots of efforts have been …
Generative partial multi-view clustering with adaptive fusion and cycle consistency
Nowadays, with the rapid development of data collection sources and feature extraction
methods, multi-view data are getting easy to obtain and have received increasing research …
methods, multi-view data are getting easy to obtain and have received increasing research …
One-pass multi-view clustering for large-scale data
Existing non-negative matrix factorization based multi-view clustering algorithms compute
multiple coefficient matrices respect to different data views, and learn a common consensus …
multiple coefficient matrices respect to different data views, and learn a common consensus …
A dirty model for multi-task learning
We consider the multiple linear regression problem, in a setting where some of the set of
relevant features could be shared across the tasks. A lot of recent research has studied the …
relevant features could be shared across the tasks. A lot of recent research has studied the …