The method of multi-step dimensionality reduction and parallel feature fusion in clothing recognition
Yutong Yu, Qing Zhu · 2016
This paper studies the parallel feature level fusion algorithm based on multiple dimension reduction. In view of the traditional serial and parallel feature fusion method shortcomings, this paper proposes a dimensionality reduction method for the feature vector using PCA (Principal Component Analysis) method before fusing the feature vector. In order to solve the high-dimensional problem after feature fusion, this paper puts forward a kind of generalized K-L transformation based on the unitary space to compress the dimension of fusion feature vector and remove redundant data.