Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11084
Title: Latent structured perceptrons for large-scale learning with hidden information
Authors: Sun, X
Matsuzaki, T
Li, W 
Keywords: Convergence analysis
Hidden information
Large-scale learning
Latent variable
Structured perceptron
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on knowledge and data engineering, 2013, v. 25, no. 9, 6226404, p. 2063-2075 How to cite?
Journal: IEEE transactions on knowledge and data engineering 
Abstract: Many real-world data mining problems contain hidden information (e.g., unobservable latent dependencies). We propose a perceptron-style method, latent structured perceptron, for fast discriminative learning of structured classification with hidden information. We also give theoretical analysis and demonstrate good convergence properties of the proposed method. Our method extends the perceptron algorithm for the learning task with hidden information, which can be hardly captured by traditional models. It relies on Viterbi decoding over latent variables, combined with simple additive updates. We perform experiments on one synthetic data set and two real-world structured classification tasks. Compared to conventional nonlatent models (e.g., conditional random fields, structured perceptrons), our method is more accurate on real-world tasks. Compared to existing heavy probabilistic models of latent variables (e.g., latent conditional random fields), our method lowers the training cost significantly (almost one order magnitude faster) yet with comparable or even superior classification accuracy. In addition, experiments demonstrate that the proposed method has good scalability on large-scale problems.
URI: http://hdl.handle.net/10397/11084
ISSN: 1041-4347
EISSN: 1558-2191
DOI: 10.1109/TKDE.2012.129
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