Imbalanced classification is one of the research hotspots in the field of machine learning， where oversampling increases minority samples through repeated extraction or artificial synthesis to rebalance the dataset. However， most of the existing oversampling methods are based on the original data distribution， and are difficult to reveal more dataset distribution characteristics. To address the above problem， firstly， an improved semi-supervised clustering algorithm was proposed to mine the data distribution characteristics. Secondly， based on the results of semi-supervised clustering， the highly-confident unlabeled data （pseudo-labeled samples） was selected from minority-class clusters to join into the original training set. In this way， in addition to rebalancing the dataset， the distribution characteristics obtained by semi-supervised clustering was able to be used to assist the imbalanced classification. Finally， the results of semi-supervised clustering and classification were fused to predict the final labels， which further improved the model performance of imbalanced classification. With G-mean and Area Under Curve （AUC） selected as evaluation indicators， the proposed algorithm was compared with seven oversampling-/undersampling-based imbalanced classification algorithms， such as TU （Trainable Undersampling） and CDSMOTE （Class Decomposition Synthetic Minority Oversampling TEchnique） on 10 public datasets. Experimental results show that compared with TU and CDSMOTE， the proposed algorithm has the average AUC increased by 6.7% and 3.9% respectively， the average G-mean improved by 7.6% and 2.1% respectively. At the same time， the proposed algorithm achieves the highest average results on both evaluation indicators than all the algorithms to be compared. It can be seen that the proposed algorithm can effectively improve the imbalanced classification performance.

Concerning the problem that the linear eigentransformation method cannot capture the statistical properties of the nonlinear facial image, a Data-driven Local Eigentransformation (DLE) method for face hallucination was proposed. Firstly, some samples most similar to the input image patch were searched. Secondly, a patch-based eigentransformation method was used for modeling the relationship between the Low-Resolution (LR) and High-Resolution (HR) training samples. Finally, a post-processing approach refined the hallucinated results. The experimental results show the proposed method has better visual performance as well as 1.81dB promotion over method of locality-constrained representation in objective evaluation criterion for face image especially with noise. This method can effectively hallucinate surveillant facial images.

To solve the Small Sample Size (SSS) problem and the "inferior" problem of traditional Fisher Linear Discriminant Analysis (FLDA) when it is applied to Synthetic Aperture Radar (SAR) image recognition tasks, a new image feature extraction technique was proposed based on weighted two-directional and two-dimensional linear discriminant analysis (W(2D)2LDA). First, the scatter matrices in the two-directional and two-dimensional linear discriminant analysis criterion were modified by adding weights. Then, feature matrix was extracted by W(2D)2LDA. The experimental results with MSTAR dataset verify the effectiveness of the proposed method, and it can strengthen the feature's discrimination and obtain better recognition performance with fewer memory requirements simultaneously.