Semi-Supervised Learning最新文献

Prediction of Protein Function from Networks 基于网络的蛋白质功能预测

Semi-Supervised Learning Pub Date : 2006-11-01 DOI: 10.7551/MITPRESS/9780262033589.003.0020

Hyunjung Shin, K. Tsuda

引用次数: 20

Graph Kernels by Spectral Transforms 谱变换的图核

Semi-Supervised Learning Pub Date : 2006-09-16 DOI: 10.7551/mitpress/9780262033589.003.0015

Xiaojin Zhu, J. Kandola, J. Lafferty, Zoubin Ghahramani

{"title":"Graph Kernels by Spectral Transforms","authors":"Xiaojin Zhu, J. Kandola, J. Lafferty, Zoubin Ghahramani","doi":"10.7551/mitpress/9780262033589.003.0015","DOIUrl":"https://doi.org/10.7551/mitpress/9780262033589.003.0015","url":null,"abstract":"Many graph-based semi-supervised learning methods can be viewed as imposing smoothness conditions on the target function with respect to a graph representing the data points to be labeled. The smoothness properties of the functions are encoded in terms of Mercer kernels over the graph. The central quantity in such regularization is the spectral decomposition of the graph Laplacian, a matrix derived from the graph's edge weights. The eigenvectors with small eigenvalues are smooth, and ideally represent large cluster structures within the data. The eigenvectors having large eigenvalues are rugged, and considered noise. Different weightings of the eigenvectors of the graph Laplacian lead to different measures of smoothness. Such weightings can be viewed as spectral transforms, that is, as transformations of the standard eigenspectrum that lead to different regularizers over the graph. Familiar kernels, such as the diffusion kernel resulting by solving a discrete heat equation on the graph, can be seen as simple parametric spectral transforms. The question naturally arises whether one can obtain effective spectral transforms automatically. In this paper we develop an approach to searching over a nonparametric family of spectral transforms by using convex optimization to maximize kernel alignment to the labeled data. Order constraints are imposed to encode a preference for smoothness with respect to the graph structure. This results in a flexible family of kernels that is more data-driven than the standard parametric spectral transforms. Our approach relies on a quadratically constrained quadratic program (QCQP), and is computationally practical for large datasets.","PeriodicalId":345393,"journal":{"name":"Semi-Supervised Learning","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131491314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51

Probabilistic Semi-Supervised Clustering with Constraints 带约束的概率半监督聚类

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0005

Sugato Basu, M. Bilenko, A. Banerjee, R. Mooney

{"title":"Probabilistic Semi-Supervised Clustering with Constraints","authors":"Sugato Basu, M. Bilenko, A. Banerjee, R. Mooney","doi":"10.7551/mitpress/9780262033589.003.0005","DOIUrl":"https://doi.org/10.7551/mitpress/9780262033589.003.0005","url":null,"abstract":"In certain clustering tasks it is possible to obtain limited supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. The resulting problem is known as semi-supervised clustering, an instance of semi-supervised learning stemming from a traditional unsupervised learning setting. Several algorithms exist for enhancing clustering quality by using supervision in the form of constraints. These algorithms typically utilize the pairwise constraints to either modify the clustering objective function or to learn the clustering distortion measure. This chapter describes an approach that employs Hidden Markov Random Fields (HMRFs) as a probabilistic generative model for semi-supervised clustering, thereby providing a principled framework for incorporating constraint-based supervision into prototype-based clustering. The HMRF-based model allows the use of a broad range of clustering distortion measures, including Bregman divergences (e.g., squared Euclidean distance, KL divergence) and directional distance measures (e.g., cosine distance), making it applicable to a number of domains. The model leads to the HMRF-KMeans algorithm which minimizes an objective function derived from the joint probability of the model, and allows unification of constraint-based and distance-based semi-supervised clustering methods. Additionally, a two-phase active learning algorithm for selecting informative pairwise constraints in a querydriven framework is derived from the HMRF model, facilitating improved clustering performance with relatively small amounts of supervision from the user.","PeriodicalId":345393,"journal":{"name":"Semi-Supervised Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123133957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 70

Metric-Based Approaches for Semi-Supervised Regression and Classification 基于度量的半监督回归和分类方法

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0023

D. Schuurmans, F. Southey, Dana F. Wilkinson, Yuhong Guo

引用次数: 14

Label Propagation and Quadratic Criterion 标签传播与二次准则

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0011

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux

引用次数: 51

The Geometric Basis of Semi-Supervised Learning 半监督学习的几何基础

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0012

Vikas Sindhwani, M. Belkin, P. Niyogi

引用次数: 36

Entropy Regularization 熵正则化

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0009

Yves Grandvalet, Yoshua Bengio

引用次数: 81

Gaussian Processes and the Null-Category Noise Model 高斯过程与零类噪声模型

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0008

Neil D. Lawrence, Michael I. Jordan

引用次数: 7

Transductive Inference and Semi-Supervised Learning 传导推理和半监督学习

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0024

V. Vapnik

引用次数: 43

Large-Scale Algorithms 大规模的算法

Semi-Supervised Learning Pub Date : 1900-01-01 DOI: 10.7551/mitpress/9780262033589.003.0018

Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux

引用次数: 1