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Sparse representation based anomaly detection algorithms have received a widely interest in recent years. However, most of the existing approaches fail to pay attention to the manifold structure of the video data, which has been pointed to be important for data representation. To overcome this limitation, we develop a new sparse coding algorithm named constrained sparse representation (CSR) for video...
A novel approach called Spectral–Spatial 1-D Manifold Embedding (SS1DME) is proposed in this paper for remotely sensed hyperspectral image (HSI) classification. This novel approach is based on a generalization of the recently developed smooth ordering model, which has gathered a great interest in the image processing area. In the proposed approach, first, we employ the spectral–spatial information-based...
In this paper, a novel classification paradigm, termed Spectral-Spatial One Dimensional Manifold Embedding (SS1DME), is proposed for classification of hyperspectral imagery (HSI). The proposed paradigm integrates the spectral affinity and spatial information into a uniform metric framework. In SS1DME, a spectral-spatial affinity metric is utilized to learn the similarity of HSI pixels. Moreover, a...
Neighborhood selection is one of the most important link in low-dimensional representations of high-dimensional data sets. Also, a good distance measure among the data points is where the shoe pinches. In this paper, we use the cam weighted distance to find a more flexible neighborhood of a data point in a newly-created space of r-isomap algorithm. It is a major advantage of r-isomap to optimize the...
In this paper, we propose a novel approach for face recognition, that combine Supervised Locality Preserving Projection (SLPP) with Maximum Margin Criterion (MMC) for preserving the within-class neighborhood structure of facial manifold and meanwhile finding an optimal feature space for classification. We also give an effective solution to the eigenvalue problem. Our method can avoid the preprocessing...
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