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Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data. This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data. Different from other relative work, we pay our attention to graph construction, which plays...
Graph plays a very important role in graph based semi-supervised learning (SSL) methods. However, most current graph construction methods emphasize on local properties of the graph. In this paper, inspired by the advances of compressive sensing, we present a novel method to construct a so-called low-rank graph (LR-graph) for graph based SSL methods. Assuming that the graph is sparse and low rank,...
In this paper, we propose Semi-Supervised pLSA(SSpLSA) for image classification. Compared with the classic non-supervised pLSA, our method overcomes the shortcoming of poor classification performance when the features of two categories are quite similar. By introducing category label information into EM algorithm, the iteration process can be directed carefully to the desired result. SS-pLSA greatly...
Images resolution plays an important role during face recognition. Low-resolution face images will reduce drastically the performance of face recognition algorithms. In this paper, we propose a novel approach for low-resolution face recognition. Our method first exacts patches with different size from the face images. Each patch is represented by its LBP feature. Then, we find the sparse representation...
One of the main drawbacks of boosting is its overfitting and poor predictive accuracy when the training dataset is small and imbalanced. In this paper, we introduce a novel learning algorithm Boost-BFKO, which combines boosting and data generation. It is suitable for small and imbalanced training datasets. To enlarge training sets, Boost-BFKO uses the adaptive Balanced Feature Knockout procedure (BFKO)...
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