Sparse matrix transform-based linear discriminant analysis for hyperspectral image classification

Peng, Jiangtao; Luo, Tiejian

doi:10.1007/s11760-015-0808-y

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…In the dimensionality reduction process of principal component analysis, accurate estimation of the mutually orthogonal eigenvector matrices is required, and this step is particularly critical in high-dimensional data matrices. Assume that the high-dimensional matrix is represented by X, where X has m vectors, each of which is of dimension p. e covariance matrix of the matrix X is A, and an unbiased estimate of A is S. To improve the accuracy of the maximum likelihood estimation of the orthogonal eigenvector matrix E after the covariance matrix eigendecomposition, Cao and Bouman [20] of Purdue University, USA, proposed a transformation [20,21] that represents the orthogonal transformation matrix as the product of a series of Givens rotations E � G 1 G 2 . .…”

Section: Sparse Matrix Transformationmentioning

confidence: 99%

A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model

Hong

Zhao

Zhu

et al. 2021

Security and Communication Networks

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With the rapid development of information technology, people benefit more and more from big data. At the same time, it becomes a great concern that how to obtain optimal outputs from big data publishing and sharing management while protecting privacy. Many researchers seek to realize differential privacy protection in massive high-dimensional datasets using the method of principal component analysis. However, these algorithms are inefficient in processing and do not take into account the different privacy protection needs of each attribute in high-dimensional datasets. To address the above problem, we design a Divided-block Sparse Matrix Transformation Differential Privacy Data Publishing Algorithm (DSMT-DP). In this algorithm, different levels of privacy budget parameters are assigned to different attributes according to the required privacy protection level of each attribute, taking into account the privacy protection needs of different levels of attributes. Meanwhile, the use of the divided-block scheme and the sparse matrix transformation scheme can improve the computational efficiency of the principal component analysis method for handling large amounts of high-dimensional sensitive data, and we demonstrate that the proposed algorithm satisfies differential privacy. Our experimental results show that the mean square error of the proposed algorithm is smaller than the traditional differential privacy algorithm with the same privacy parameters, and the computational efficiency can be improved. Further, we combine this algorithm with blockchain and propose an Efficient Privacy Data Publishing and Sharing Model based on the blockchain. Publishing and sharing private data on this model not only resist strong background knowledge attacks from adversaries outside the system but also prevent stealing and tampering of data by not-completely-honest participants inside the system.

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Section: Sparse Matrix Transformationmentioning

confidence: 99%

A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model

Hong

Zhao

Zhu

et al. 2021

Security and Communication Networks

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“…LDA is a normally used method for classification [37][38][39]. Suppose a set of N samples {x 1 , x 2 , .…”

Section: Ldamentioning

confidence: 99%

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Liu

et al. 2019

Remote Sensing

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Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

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“…PCA has been widely used for HSI analysis to obtain the principal component features. Linear Discriminant Analysis (LDA) is a classic supervised method to obtain strong discriminant features of HSI (Peng and Luo 2016). LDA utilizes the priori class label to maximize the between-class variance and minimize the within-class variance which will separate the inter-class samples and compact the intra-class samples.…”

Section: Introductionmentioning

confidence: 99%

Review on graph learning for dimensionality reduction of hyperspectral image

Zhang

Luo

2020

Geo-spatial Information Science

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Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction for a hyperspectral image. Firstly, we review the development of graph learning and its application in a hyperspectral image. Then, we mainly discuss several representative graph methods including two manifold learning methods, two sparse graph learning methods, and two hypergraph learning methods. For manifold learning, we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph. For sparse graph, we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph. For hypergraph learning, we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.

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Sparse matrix transform-based linear discriminant analysis for hyperspectral image classification

Cited by 13 publications

References 20 publications

A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model

A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Review on graph learning for dimensionality reduction of hyperspectral image

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