A Family of Maximum Margin Criterion for Adaptive Learning

TL;DR

This paper introduces an improved maximum margin criterion (MMC) and its variants, enabling adaptive learning and deep feature extraction for complex, high-dimensional data in real-world applications.

Contribution

The paper proposes a new MMC definition and variants, including MMC network for deep learning, enhancing discriminant ability in adaptive, high-dimensional data scenarios.

Findings

MMC variants outperform traditional methods in complex data tasks.

MMC network effectively learns deep features from images.

Experimental results validate the discriminant power of proposed methods.

Abstract

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.