NDLI: Transfer Learning via Cluster Correspondence Inference

Content Provider	IEEE Xplore Digital Library
Author	Mingsheng Long Wei Cheng Xiaoming Jin Jianmin Wang Dou Shen
Copyright Year	2010
Abstract	Transfer learning targets to leverage knowledge from one domain for tasks in a new domain. It finds abundant applications, such as text/sentiment classification. Many previous works are based on cluster analysis, which assume some common clusters shared by both domains. They mainly focus on the one-to-one cluster correspondence to bridge different domains. However, such a correspondence scheme might be too strong for real applications where each cluster in one domain corresponds to many clusters in the other domain. In this paper, we propose a Cluster Correspondence Inference (CCI) method to iteratively infer many-to-many correspondence among clusters from different domains. Specifically, word clusters and document clusters are exploited for each domain using nonnegative matrix factorization, then the word clusters from different domains are corresponded in a many-to-many scheme, with the help of shared word space as a bridge. These two steps are run iteratively and label information is transferred from source domain to target domain through the inferred cluster correspondence. Experiments on various real data sets demonstrate that our method outperforms several state-of-the-art approaches for cross-domain text classification.
Starting Page	917
Ending Page	922
File Size	225932
Page Count	6
File Format	PDF
ISBN	9781424491315
ISSN	15504786
DOI	10.1109/ICDM.2010.146
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Bridges Support vector machines Accuracy Joints Clustering algorithms Convergence Cluster Correspondence Inference Transfer Learning Text Classification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Combining Classification and Clustering for Tweet Sentiment Analysis

Support vector machines based on hyper-ball clustering

An Internet Traffic Classification Method Based on Semi-Supervised Support Vector Machine

Optimize the Large Scale SVM Social Spam Detection Model via Bistratal Reduction Method

Parallelizing multiclass Support Vector Machines for scalable image annotation

Cluster Based Training for Scaling Non-linear Support Vector Machines

Class decomposition via clustering: a new framework for low-variance classifiers

Unlabeled data classification via support vector machines and k-means clustering

Self-adaptive kernel machine: online clustering in RKHS

Transfer Learning via Cluster Correspondence Inference

Similar Documents

Combining Classification and Clustering for Tweet Sentiment Analysis

Support vector machines based on hyper-ball clustering

An Internet Traffic Classification Method Based on Semi-Supervised Support Vector Machine

Optimize the Large Scale SVM Social Spam Detection Model via Bistratal Reduction Method

Parallelizing multiclass Support Vector Machines for scalable image annotation

Cluster Based Training for Scaling Non-linear Support Vector Machines

Class decomposition via clustering: a new framework for low-variance classifiers

Unlabeled data classification via support vector machines and k-means clustering

Self-adaptive kernel machine: online clustering in RKHS

Transfer Learning via Cluster Correspondence Inference