This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Nov. 2012 (vol. 34 no. 11)
pp. 2216-2232
Tingting Mu, University of Manchester, Manchester
John Yannis Goulermas, University of Liverpool, Liverpool
Jun'ichi Tsujii, Microsoft Research Asia, China
Sophia Ananiadou, University of Manchester, Manchester
This paper is about supervised and semi-supervised dimensionality reduction (DR) by generating spectral embeddings from multi-output data based on the pairwise proximity information. Two flexible and generic frameworks are proposed to achieve supervised DR (SDR) for multilabel classification. One is able to extend any existing single-label SDR to multilabel via sample duplication, referred to as MESD. The other is a multilabel design framework that tackles the SDR problem by computing weight (proximity) matrices based on simultaneous feature and label information, referred to as MOPE, as a generalization of many current techniques. A diverse set of different schemes for label-based proximity calculation, as well as a mechanism for combining label-based and feature-based weight information by considering information importance and prioritization, are proposed for MOPE. Additionally, we summarize many current spectral methods for unsupervised DR (UDR), single/multilabel SDR, and semi-supervised DR (SSDR) and express them under a common template representation as a general guide to researchers in the field. We also propose a general framework for achieving SSDR by combining existing SDR and UDR models, and also a procedure of reducing the computational cost via learning with a target set of relation features. The effectiveness of our proposed methodologies is demonstrated with experiments with document collections for multilabel text categorization from the natural language processing domain.
Index Terms:
Laplace equations,Optimization,Principal component analysis,Kernel,Symmetric matrices,Natural language processing,Vectors,embeddings,Dimensionality reduction,supervised,semi-supervised,multilabel classification
Citation:
Tingting Mu, John Yannis Goulermas, Jun'ichi Tsujii, Sophia Ananiadou, "Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2216-2232, Nov. 2012, doi:10.1109/TPAMI.2012.20
Usage of this product signifies your acceptance of the Terms of Use.