A Family of Maximum Margin Criterion for Adaptive Learning
This work addresses pattern analysis challenges in data mining and recognition for real-world applications, but it appears incremental as it builds on existing MMC methods.
The authors tackled the problem of high-dimensional and large-scale data in pattern analysis by introducing an improved maximum margin criterion (MMC) method and its variants, such as random MMC and MMC networks, to enable adaptive learning, with experimental results showing competitive discriminant ability on diverse datasets.
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.