Journal Article10.1016/J.NEUCOM.2017.09.039
Nuclear-norm based semi-supervised multiple labels learning
Yang Liu,Feiping Nie,Quanxue Gao +2 more
14
TL;DR: This paper proposes a novel nuclear-norm based semi-supervised learning framework for multi-label classification that effectively improves the classification performance and introduces a non-greedy iterative algorithm to solve the criterion function.
read more
About: This article is published in Neurocomputing. The article was published on 31 Jan 2018. The article focuses on the topics: Semi-supervised learning & Stability (learning theory).
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A survey of multi-label classification based on supervised and semi-supervised learning
TL;DR: This paper first review supervised learning classification algorithms in terms of label non-correlation and label correlation and semi-supervised learning classification algorithm in Terms of inductive methods and transductive methods, and research directions in complex concept drift, label complex correlation, feature selection and class imbalance are presented.
46
Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model.
TL;DR: An optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space.
33
Low rank label subspace transformation for multi-label learning with missing labels
Sanjay Kumar,Reshma Rastogi +1 more
TL;DR: In this article , a unified framework that captures the label correlations utilizing both auxiliary label matrix and the low rank constraints on estimated labels is proposed, which also enforces maximal separation among different label subspaces for better label differentiation.
28
Nuclear-norm based 2DLDA with application to face recognition
TL;DR: Compared with most existing robust 2DLDA methods, the proposed nuclear-norm based two-dimensional Linear Discriminant analysis (2DLDA-nuclear) not only helps suppress noise and illumination but also preserves spatial geometric structure of image.
18
Optimally Connected Deep Belief Net for Click Through Rate Prediction in Online Advertising
TL;DR: A novel model named optimally connected deep belief net (OCDBN) for click prediction with rotation codes whitening technology based on optimal mean removal is proposed, which significantly outperforms the existing models in accuracy, coefficient of determination, sparsity, and perplexity of click prediction.
14
References
Term Weighting Approaches in Automatic Text Retrieval
Gerard Salton,Chris Buckley +1 more
TL;DR: This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.
Robust principal component analysis
TL;DR: In this paper, the authors prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm.
Exact Matrix Completion via Convex Optimization
TL;DR: It is proved that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries, and that objects other than signals and images can be perfectly reconstructed from very limited information.
•Proceedings Article
A Comparative Study on Feature Selection in Text Categorization
Yiming Yang,Jan O. Pedersen +1 more
- 08 Jul 1997
TL;DR: This paper finds strong correlations between the DF IG and CHI values of a term and suggests that DF thresholding the simplest method with the lowest cost in computation can be reliably used instead of IG or CHI when the computation of these measures are too expensive.
5.6K
•Proceedings Article
Learning with Local and Global Consistency
Dengyong Zhou,Olivier Bousquet,TN Lal,Jason Weston,Bernhard Schölkopf +4 more
- 09 Dec 2003
TL;DR: A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points.