Maximum Margin Projection Subspace Learning for Visual Data Analysis

Symeon Nikitidis, Anastasios Tefas, Ioannis Pitas
2014 IEEE Transactions on Image Processing  
Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin
more » ... maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms. Index Terms-Maximum margin projections, support vector machines, face recognition, facial expression recognition, object recognition. 1057-7149
doi:10.1109/tip.2014.2348868 pmid:25148664 fatcat:vtmo3xxupjbjtdxivxhuwazp3y