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New paper on dimensionality reduction for multi-label classification

2012-01-16

We are pleased to announce the publication on a new journal article about dimensionality reduction methods. The article demonstrates the applicability of such methods to natural language processing and text mining tasks, through experiments carried out on multi-label text categorization using document collections.

Mu, T., Goulermas, J. Y, Tsujii, J. and Ananiadou, S. (2012). Proximity-based Frameworks for Generating Embeddings from Multi-Output Data. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Full abstract: 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 multi-label classification. One is able to extend any existing single-label SDR to multi-label via sample duplication, referred to as MESD. The other is a multi-label design framework that tackles the SDR problem by computing weight 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/multi-label 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.