Multi-view learning for multivariate performance measures optimization

TL;DR

This paper introduces a novel multi-view learning framework that optimizes complex multivariate performance measures by combining linear discriminant functions from multiple views and employing an iterative cutting-plane optimization method.

Contribution

It proposes a new approach to optimize multivariate performance measures from multi-view data using a combined linear discriminant model and an iterative cutting-plane algorithm.

Findings

Effective optimization of multivariate measures from multi-view data.

Improved performance through view response consistency.

Iterative algorithm converges efficiently.

Abstract

In this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. This problem has two features: the data points are presented by multiple views, and the target of learning is to optimize complex multivariate performance measures. We propose to learn a linear discriminant functions for each view, and combine them to construct a overall multivariate mapping function for mult-view data. To learn the parameters of the linear dis- criminant functions of different views to optimize multivariate performance measures, we formulate a optimization problem. In this problem, we propose to minimize the complexity of the linear discriminant functions of each view, encourage the consistences of the responses of different views over the same data points, and minimize the upper boundary of a given multivariate performance measure. To optimize this problem, we employ the cutting-plane method in an iterative algorithm. In each iteration, we update a set of constrains, and optimize the mapping function parameter of each view one by one.