PAC-Bayes analysis of multi-view learning
TLDR
In this article, the authors present eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers, which are derived from two derived logarithmic determinant inequalities whose difference lies in whether the dimensionality of data is involved.About:
This article is published in Information Fusion.The article was published on 2017-05-01 and is currently open access. It has received 44 citations till now. The article focuses on the topics: Statistical learning theory & Prior probability.read more
Citations
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Journal ArticleDOI
Multi-view learning overview
TL;DR: This overview reviews theoretical underpinnings of multi-view learning and attempts to identify promising venues and point out some specific challenges which can hopefully promote further research in this rapidly developing field.
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GMC: Graph-Based Multi-View Clustering
TL;DR: The proposed general Graph-based Multi-view Clustering (GMC) takes the data graph matrices of all views and fuses them to generate a unified graph matrix, which helps partition the data points naturally into the required number of clusters.
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A study of graph-based system for multi-view clustering
TL;DR: A novel multi-view clustering method that works in the GBS framework is also proposed, which can construct data graph matrices effectively, weight each graph matrix automatically, and produce clustering results directly.
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Multiview dimension reduction via Hessian multiset canonical correlations
TL;DR: HesMCC is presented, which takes the advantage of Hessian and provides superior extrapolating capability and finally leverage the performance of TCCA, KMUDA, MCCA and LapMCC for multiview dimension reduction.
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Multiview learning for understanding functional multiomics.
Nam D. Nguyen,Daifeng Wang +1 more
TL;DR: This review introduces multiview learning—an emerging machine learning field—and envisions its potentially powerful applications to multiomics and discusses the potential applications of each method, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics.
References
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Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
Gaussian Processes for Machine Learning
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Book
Kernel Methods for Pattern Analysis
TL;DR: This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Proceedings ArticleDOI
Combining labeled and unlabeled data with co-training
Avrim Blum,Tom M. Mitchell +1 more
TL;DR: A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.