Maximum Margin Criterion (MMC) based Feature Extraction method is more efficient than LDA for calculating the discriminant vectors since it does not need to calculate the inverse ...
Wankou Yang, Jianguo Wang, Mingwu Ren, Jingyu Yang
This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass featu...
In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper...
Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discove...
Deng Cai, Xiaofei He, Kun Zhou, Jiawei Han, Hujun ...