Manifold learning in sparse selected feature subspaces
Yuchun Fang, Yandan Zheng, Chanjuan Yu · 2015
Feature selection and extraction are often combined for learning effective representation in image recognition. In this paper, we propose a serial combination method for feature representation in face image recognition. The algorithm first selects a subset in high-dimensional low level feature spaces with sparse learning and then feature extraction is further performed with manifold learning. Both feature selection and extraction are performed evolving class-specific information to learn task-specific feature representation. The serial combination serves to form the final lower dimensional representation in a multi-layer structure. Experimental analysis for multiple face image recognition tasks prove the effectiveness of the proposed serial combination of feature learning.