Locality preserving projection based on Euler representation

Long, Tianhang; Sun, Yanfeng; Gao, Junbin; Hu, Yongli; Yin, Baocai

doi:10.1016/j.jvcir.2020.102796

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Roweis S [19], He X [20], Feng L [21] and other scholars have done similar work and achieved good results. These methods include principal component analysis (PCA) [22][23], locally linear embedding (LLE) [24][25][26], and locality preserving projections (LPP) [27][28][29] among others [30][31]. All of them aim to maintain the correlations of the samples and then find the intrinsic space in which the samples lie.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Efficient Max-min Metric for Classification

Liu

2023

J. Phys.: Conf. Ser.

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Distance metric learning is an important method to study distance metrics that reflect the interaction between features and labels. Because of the high computational complexity and the fact that existing studies on algorithms that measure the similarities with Euclidean distances cannot reflect the real correlations between pairs of samples, learning a suitable distance metric is quite demanding for many data mining tasks. This paper innovatively proposes an extended efficient max-min metric (EMM) that maximizes the total distance between different pairs and minimizes the total distance between similar pairs as much as possible. Simultaneously, the adoption of the local preserving projection framework changes the solution process of the algorithm and improves the speed of the algorithm without losing accuracy. Because traditional EMM only considers pairwise constraints and ignores sample distribution, this study extends EMM based on sample distribution and successfully solves the multi-manifold problem. In the process of data realization, compared with the vector representation method, the use of high-order tensors will make the image representation more accurate and natural. To maintain the structure of higher-order tensors, a tensor-efficient max-min metric (TEMM) is proposed. In order to prove the accuracy and superiority of the research method in this paper, a large number of experiments have been carried out on image processing. The experimental results show that the method proposed in this paper has a good effect.

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Section: Introductionmentioning

confidence: 99%

A Novel Method of Efficient Max-min Metric for Classification

Liu

2023

J. Phys.: Conf. Ser.

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“…Despite some advantages, PCA is not suitable for manifoldstructure data because it is a linear method that only considers the global Euclidean structure of samples. us, some manifold-based dimension reduction methods such as locality preserving projection (LPP) [18], neighborhood preserving embedding (NPE) [19], and sparsity preserving projections (SPP) [20] are presented to deal with dimension reduction related to manifold-structure samples, with a view to preserve geometric structures of original dataset in the subspace.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis

Zhou

Zhu

2022

Shock and Vibration

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Rolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time consumption. Thus, it is of crucial significance in extracting optimal features from high-dimensional and redundant feature space for classification. In this study, a fault diagnosis of rolling bearings model based on sparse principal subspace discriminant analysis is proposed. It extracts sparse discrimination information, meanwhile preserving the main energy of original dataset, and the sparse regularization term and sparse error term constrained by l2,1-norm are introduced to improve the performance of feature extraction and the robustness to noise and outliers. The multi-domain feature space involved a time domain, frequency domain, and time-frequency domain is first derived from the original vibration signals. Then, the intrinsic geometric features extracted by sparse principal subspace discriminant analysis are fed into a support vector machine classifier to recognize different operating conditions of bearings. The experimental results demonstrated that the feasibility and effectiveness of the proposed fault diagnosis model based on a sparse principal subspace discriminant analysis algorithm can achieve higher recognition accuracy than fisher discriminant analysis and its extensions, and it is relatively insensitive to the impact of noise and outliers owing to the sparse property.

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Coupled locality discriminant analysis with globality preserving for dimensionality reduction

Zhu

et al. 2022

Appl Intell

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Locality preserving projection based on Euler representation

Cited by 9 publications

References 17 publications

A Novel Method of Efficient Max-min Metric for Classification

A Novel Method of Efficient Max-min Metric for Classification

Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis

Coupled locality discriminant analysis with globality preserving for dimensionality reduction

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