Regularized coplanar discriminant analysis for dimensionality reduction

Huang, Ke-Kun; Dai, Dao‐Qing; Ren, Chuan-Xian

doi:10.1016/j.patcog.2016.08.024

Cited by 34 publications

(10 citation statements)

References 52 publications

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“…where α is a compromise parameter. W ij is the spatial-spectral matrix in Equation (14) and G ij is still the weight matrix representing the nearest neighbor relationship. If x i and x j are neighbors,…”

Section: Iss-wme Modelmentioning

confidence: 99%

“…The purpose of the MDR method in HSI is to find the manifold structure in the high-dimensional space. LLE [14] obtains the reconstruction weight by characterizing the local adjacency sample of the data and keeps the neighborhood relationship in the local range unchanged when mapping to the low-dimensional space. However, an LLE algorithm only determines the neighbor relationship between points and cannot describe the structural features of data.…”

Section: Introductionmentioning

confidence: 99%

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Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Liu

Xia

et al. 2020

Sensors

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Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial–spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial–spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.

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Section: Iss-wme Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Liu

Xia

et al. 2020

Sensors

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show abstract

“…As described in the aforementioned analysis, SPP ignores the label information of samples and cannot well exhibit the local neighborhood relationship of adjacent graph. Although a few improved algorithms [39][40][41][42] have been proposed, their performance is still limited. In this paper, we propose a DSGE which consists of two improvements on adjacent graph construction and low dimensional projection, respectively.…”

Section: Discriminative Sparsity Graph Embeddingmentioning

confidence: 99%

“…These above supervised learning methods [39][40][41][42] utilize the local neighborhood information of intra-class samples and inter-class samples respectively, but they ignore the global distribution information of all samples in space. In fact, researchers have shown that the global geometric structure of data sets implies useful discriminative information which is important for image identification [43,44].…”

Section: Introductionmentioning

confidence: 99%

Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition

2019

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In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influence between the given sample and the pseudo-neighbor samples with the different classes. Then, the global distribution constraints are introduced to the projection objective function for seeking the optimal subspace which compacts intra-classes samples and alienates inter-classes samples at the same time. Extensive experiments are carried out on AR, Extended Yale B, LFW and PubFig databases which are four representative face datasets, and the corresponding experimental results illustrate the effectiveness of our proposed method.

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“…Supervised algorithms conduct datasets with labels that aim to present better performance and low complexity. Linear discriminant analysis (LDA), local discriminant embedding (LDE) [ 7 ], discriminant sparse neighborhood preserving embedding (DSNPE) [ 8 ], regularized coplanar discriminant analysis (RCDA) [ 9 ], marginal Fisher analysis (MFA) [ 3 , 5 , 10 ], discriminant neighborhood embedding (DNE) [ 11 ], locality-based discriminant neighborhood embedding(LDNE) [ 12 ], and double adjacency graphs-based discriminant neighborhood embedding (DAG-DNE) [ 13 ] are typical supervised algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Discriminant Analysis

Ding

et al. 2018

Sensors

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The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

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Regularized coplanar discriminant analysis for dimensionality reduction

Cited by 34 publications

References 52 publications

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition

Hierarchical Discriminant Analysis

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