计算机科学
嵌入
非线性降维
模式识别(心理学)
人工智能
图嵌入
图形
降维
歧管(流体力学)
稀疏逼近
判别式
拉普拉斯矩阵
歧管对齐
作者
Yang Yang,Zhengqun Wang,Chunlin Xu,Yan Chen
出处
期刊:International Conference Graphics and Image Processing
日期:2019-05-06
摘要
Sparse discriminative multi manifold embedding (SDMME) algorithm was used for feature extraction, graph construction and projection learning were independent, the quality of the graph directly affects the effect of projection learning. In order to solve the problem, a new algorithm named sparse discriminative multi manifold embedding based on graph optimization (GOSDMME) was proposed in this paper. First, in proposed approach, the image matrix was divided into blocks. The matrix blocks on the same image were located on the same manifold. Then, the sparse graph was used to establish the connection relationship between different blocks. Finally, in the framework of the same objective function, the sparse constraint graphs and projections were studied simultaneously. The graphs and projections were learned at the same time, iterate and update the graph and projection to obtain a projection matrix that satisfies the accuracy requirements. The face recognition experiments conducted on Extended Yale B and CMU PIE datasets show that the new algorithm has better recognition performance than the SDMME algorithm.
科研通智能强力驱动
Strongly Powered by AbleSci AI