Modified UDP-Based Semi-supervised Learning for Fruit Internal Quality Detection

摘要

In order to ignore the limitation of local structure features for the traditional linear dimensionality reduction methods, a new semi-supervised manifold learning is proposed for apple mealiness detection. Assuming the character of the hyperspectral scattering images, an unsupervised non-linear dimensionality reduction method unsupervised discriminant projection (UDP) coupled with sample label information and then develop a semi-supervised learning algorithm, which can keep the local and global structure and can take advantage of the important label information, then get geometric structure optimal linear projection. The classification results with PCA-MUDP are compared with some traditional algorithm. To the two-class classification of 'mealy' and 'non-mealy' apples, the results show that PCA-MUDP is better than the others.