On multi-view learning with additive models

TL;DR

This paper introduces a generalized additive modeling framework for multi-view transductive learning, effectively integrating multiple data views and unlabeled data, with efficient algorithms and view selection criteria, demonstrating competitive performance on various datasets.

Contribution

It proposes a comprehensive fixed point additive model for multi-view transductive learning, including view selection via AIC and efficient fitting algorithms for semi-supervised graph-based data.

Findings

Model performs well on synthetic and real datasets.

Competitive with state-of-the-art co-training methods.

Provides a flexible framework for multi-view semi-supervised learning.

Abstract

In many scientific settings data can be naturally partitioned into variable groupings called views. Common examples include environmental (1st view) and genetic information (2nd view) in ecological applications, chemical (1st view) and biological (2nd view) data in drug discovery. Multi-view data also occur in text analysis and proteomics applications where one view consists of a graph with observations as the vertices and a weighted measure of pairwise similarity between observations as the edges. Further, in several of these applications the observations can be partitioned into two sets, one where the response is observed (labeled) and the other where the response is not (unlabeled). The problem for simultaneously addressing viewed data and incorporating unlabeled observations in training is referred to as multi-view transductive learning. In this work we introduce and study a comprehensive generalized fixed point additive modeling framework for multi-view transductive learning, where any view is represented by a linear smoother. The problem of view selection is discussed using a generalized Akaike Information Criterion, which provides an approach for testing the contribution of each view. An efficient implementation is provided for fitting these models with both backfitting and local-scoring type algorithms adjusted to semi-supervised graph-based learning. The proposed technique is assessed on both synthetic and real data sets and is shown to be competitive to state-of-the-art co-training and graph-based techniques.