Semi-supervised learning of local structured output predictors

TL;DR

This paper introduces a semi-supervised learning approach that learns local structured output predictors for different data neighborhoods, improving prediction accuracy by capturing local data distributions.

Contribution

It proposes a novel method to learn multiple local predictors for neighborhoods, addressing the limitations of single global predictors in semi-supervised structured output prediction.

Findings

Outperforms existing methods on four benchmark datasets

Effectively learns local predictors for diverse data distributions

Demonstrates advantages in semi-supervised structured output tasks

Abstract

In this paper, we study the problem of semi-supervised structured output prediction, which aims to learn predictors for structured outputs, such as sequences, tree nodes, vectors, etc., from a set of data points of both input-output pairs and single inputs without outputs. The traditional methods to solve this problem usually learns one single predictor for all the data points, and ignores the variety of the different data points. Different parts of the data set may have different local distributions, and requires different optimal local predictors. To overcome this disadvantage of existing methods, we propose to learn different local predictors for neighborhoods of different data points, and the missing structured outputs simultaneously. In the neighborhood of each data point, we proposed to learn a linear predictor by minimizing both the complexity of the predictor and the upper bound of the structured prediction loss. The minimization is conducted by gradient descent algorithms. Experiments over four benchmark data sets, including DDSM mammography medical images, SUN natural image data set, Cora research paper data set, and Spanish news wire article sentence data set, show the advantages of the proposed method.