Performance Of Dimensionality Reduction Research Articles

An Intelligent Sorting Method of Film in Cotton Combining Hyperspectral Imaging and the AlexNet-PCA Algorithm.

Long-staple cotton from Xinjiang is renowned for its exceptional quality. However, it is susceptible to contamination with plastic film during mechanical picking. To address the issue of tricky removal of film in seed cotton, a technique based on hyperspectral images and AlexNet-PCA is proposed to identify the colorless and transparent film of the seed cotton. The method consists of black and white correction of hyperspectral images, dimensionality reduction of hyperspectral data, and training and testing of convolutional neural network (CNN) models. The key technique is to find the optimal way to reduce the dimensionality of the hyperspectral data, thus reducing the computational cost. The biggest innovation of the paper is the combination of CNNs and dimensionality reduction methods to achieve high-precision intelligent recognition of transparent plastic films. Experiments with three dimensionality reduction methods and three CNN architectures are conducted to seek the optimal model for plastic film recognition. The results demonstrate that AlexNet-PCA-12 achieves the highest recognition accuracy and cost performance in dimensionality reduction. In the practical application sorting tests, the method proposed in this paper achieved a 97.02% removal rate of plastic film, which provides a modern theoretical model and effective method for high-precision identification of heteropolymers in seed cotton.

Joint Dynamic Manifold and Discriminant Information Learning for Feature Extraction.

Neighborhood reconstruction is a good recipe to learn the local manifold structure. Representation-based discriminant analysis methods normally learn the reconstruction relationship between each sample and all the other samples. However, reconstruction graphs constructed in these methods have three limitations: 1) they cannot guarantee the local sparsity of reconstruction coefficients; 2) heterogeneous samples may own nonzero coefficients; and 3) they learn the manifold information prior to the process of dimensionality reduction. Due to the existence of noise and redundant features in the original space, the prelearned manifold structure may be inaccurate. Accordingly, the performance of dimensionality reduction would be affected. In this article, we propose a joint model to simultaneously learn the affinity relationship, reconstruction relationship, and projection matrix. In this model, we actively assign neighbors for each sample and learn the inter-reconstruction coefficients between each sample and their neighbors with the same label information in the process of dimensionality reduction. Specifically, a sparse constraint is employed to ensure the sparsity of neighbors and reconstruction coefficients. The whitening constraint is imposed on the projection matrix to remove the relevance between features. An iterative algorithm is proposed to solve this method. Extensive experiments on toy data and public datasets show the superiority of the proposed method.

SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field. The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI.

Robust Dimensionality Reduction via Low-rank Laplacian Graph Learning

Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, which leads to unsatisfying data processing performance. We propose a novel robust dimensionality reduction method via low-rank Laplacian graph learning for classification and clustering tasks to solve the above problem. First, we construct a low-rank Laplacian graph by combining manifold learning and subspace learning. This graph can capture both global and local structural information of the data. And we introduce rank constraints for the Laplacian graph to make it more discriminative. Second, we put the learning of projection matrix and sample affinity graph into a unified framework. The projection matrix is embedded into a robust low-rank Laplacian graph so that the low-dimensional mapping of data can maintain the structural information in the graph well. Finally, we add a regularization term to the projection matrix to make it have the ability of both feature extraction and feature selection. Therefore, the proposed model can resist the interference of noise or data damage to learn the optimal projection to achieve better performance in dimensionality reduction through such a data dimensionality reduction joint framework. Comprehensive experiments on various benchmark datasets with varying degrees of occlusions or corruptions are carried out to evaluate the performance of the proposed method. Compared with the state-of-the-art dimensionality reduction methods in the literature, the experimental results are inspiring, showing our method’s effectiveness and robustness in classification and clustering, especially in object recognition scenarios with noise or occlusions.

Refined Composite Multiscale Fluctuation Dispersion Entropy and Supervised Manifold Mapping for Planetary Gearbox Fault Diagnosis

A novel fault diagnosis scheme was developed to address the difficulty of feature extraction for planetary gearboxes using refined composite multiscale fluctuation dispersion entropy (RCMFDE) and supervised manifold mapping. The RCMFDE was first utilized in this scheme to fully mine fault features from planetary gearbox signals under multiple scales. Subsequently, as a supervised manifold mapping method, supervised isometric mapping (S-Iso) was applied to decrease the dimensions of the original features and remove redundant information. Lastly, the marine predator algorithm-based support vector machine (MPA-SVM) classifier was employed to achieve intelligent fault diagnosis of planetary gearboxes. The suggested RCMFDE combines the composite coarse-grained construction and refined computing technology, overcoming unstable and invalid entropy in the traditional multiscale fluctuation dispersion entropy. Simulation experiments and fault diagnosis experiments from a real planetary gearbox drive system show that the complexity measure capability and feature extraction effectiveness of the proposed RCMFDE outperform the multiscale fluctuation dispersion entropy (MFDE) and multi-scale permutation entropy (MPE). The S-Iso’s visualization results and dimensionality reduction performance are better than principal components analysis (PCA), linear discriminant analysis (LDA), and isometric mapping (Isomap). Moreover, the suggested fault diagnosis scheme has an accuracy rate of 100% in identifying bearing and gear defects in planetary gearboxes.

Joint neighborhood preserving and projected clustering for feature extraction

Neighborhood reconstruction is proved effective for dimensionality reduction because of the preservation of manifold structure. Conventional neighborhood preserving embedding (NPE) method first learns the affinity relationship or reconstruction relationship in the original space, and then learns the projection matrix to preserve the learned local information in low-dimensional space. However, the pre-learned manifold information may be inaccurate due to the noises and irrelevant features in real-world data. The performance of dimensionality reduction would be influenced as well. Besides, NPE and its variants only aim to preserve the local reconstruction relationship but ignore the fuzzy membership relationship between samples and cluster prototypes. To address these issues, we propose an adaptive neighborhood preserving discriminant projection model, where sparse reconstruction coefficients are updated in the process of dimensionality reduction to eliminate the influence of noises and irrelevant features. Meanwhile, we also learn the fuzzy membership relationships between data points and cluster prototypes to gather the samples belonging to the same class together in low-dimensional space. Neighborhood reconstruction learning and clustering are seamlessly connected in the learned subspace. To solve this model, an iterative algorithm is developed. The experimental results of recognition accuracy show the superiorities of the proposed methods over the state-of-the-arts.

An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

Multiview Learning for Subsurface Defect Detection in Composite Products: A Challenge on Thermographic Data Analysis

Nondestructive testing (NDT) is an economical way of detecting subsurface defects in composite products. Infrared thermography serves as a popular NDT method due to its high efficiency and low cost. However, defect identification by directly visualizing thermal images is difficult owing to the nonuniform background and noise. Recently, data analysis methods have been introduced to thermal image processing, including principal component analysis, which is known for its good performance in dimensionality reduction, feature extraction, and noise reduction. However, most of these methods can only extract linear features. In this article, a multiview learning-based autoencoder, which can process not only nonlinear features but also sequential attributes, is utilized in thermographic data analysis. After extracting the low-dimensional features by multiview learning, a background elimination step is conducted to highlight the locations and shapes of the defects. The experimental results demonstrate the feasibility of the proposed method.

A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection

Intrusion detection systems (IDS) can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols. To deal with the dynamic and complex nature of cyber-attacks, advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning (ML) and deep learning (DL). Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection capacity. However, just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security experts. Many papers have explored various dimensionality reduction algorithms, however a large number of selected features is still required to detect the attacks successfully, which humans cannot intuitively or immediately understand. To enhance user’s trust and understanding on data without sacrificing on accuracy, this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk indicator. To achieve this, a novel feature extraction pipeline was implemented consisting of the following components: (i) a fuzzy allocation scheme that transforms raw data to fuzzy class memberships, (ii) a novel modality transformation mechanism for converting feature vectors to images (Vec2im) and (iii) a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature space. The performance of the proposed methodology was validated with respect to detection accuracy, dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness (86.64% testing accuracy using only one feature) over a number of well-known feature selection (FS) and extraction techniques. The output of the proposed feature extraction pipeline could be potentially used by security experts as an indicator of malicious activity, whereas the generated images could be further utilized and/or integrated as a visual analytics tool in existing IDS.

Screening for chronic conditions with reproductive factors using a machine learning based approach

A large proportion of cases with chronic conditions including diabetes or pre-diabetes, hypertension and dyslipidemia remain undiagnosed. To include reproductive factors (RF) might be able to improve current screening guidelines by providing extra effectiveness. The objective is to study the relationships between RFs and chronic conditions’ biomarkers. A cross-sectional study was conducted. Demographics, RFs and metabolic biomarkers were collected. The relationship of the metabolic biomarkers were shown by correlation analysis. Principal component analysis (PCA) and autoencoder were compared by cross-validation. The better one was adopted to extract a single marker, the general chronic condition (GCC), to represent the body’s chronic conditions. Multivariate linear regression was performed to explore the relationship between GCC and RFs. In total, 1,656 postmenopausal females were included. A multi-layer autoencoder outperformed PCA in the dimensionality reduction performance. The extracted variable by autoencoder, GCC, was verified to be representative of three chronic conditions (AUC for patoglycemia, hypertension and dyslipidemia were 0.844, 0.824 and 0.805 respectively). Linear regression showed that earlier age at menarche (OR = 0.9976) and shorter reproductive life span (OR = 0.9895) were associated with higher GCC. Autoencoder performed well in the dimensionality reduction of clinical metabolic biomarkers. Due to high accessibility and effectiveness, RFs have potential to be included in screening tools for general chronic conditions and could enhance current screening guidelines.

Graph steered discriminative projections based on collaborative representation for Image recognition

Dimensionality reduction techniques are commonly used for image recognition. We propose a graph steered dimensionality reduction method called Discriminative Projections based on Collaborative Representation (DPCR) by transforming the dimensionality reduction task into a graph embedding framework. DPCR utilizes the collaborative representation to construct within-class and between-class graphs. To improve the discriminative performance of dimensionality reduction, DPCR introduces the label information into graph building. The novel method not only avoids the difficulty of finding proper neighborhood but also inherits the merits of manifold learning methods and the robustness of collaborative representation techniques. Experiments on benchmark datasets demonstrate its effectiveness.

A critical study of different dimensionality reduction methods for gear crack degradation assessment under different operating conditions

Gear cracks are some of the most common faults found in industrial machinery. Identification of different gear crack levels is beneficial to assessing gear crack degradation and preventing any unexpected machine breakdowns. In this paper, redundant statistical features are extracted from binary wavelet packet transform at different decomposition levels to describe different gear crack levels. Because the dimensionality of the extracted redundant statistical parameters is high to 620, it is necessary to reduce their dimensionality prior to the use of any statistical model for intelligently identifying different gear crack levels. The major idea of dimensionality reduction is that the extracted redundant statistical features in a high-dimensional space are mapped to a few significant features in a low-dimensional space, where these significant features are used to represent different gear crack levels. As of today, there are many popular linear and non-linear dimensionality reduction methods including principal component analysis, kernel principal components analysis, Isomap, Laplacian Eigenmaps and local linear embedding. Different dimensionality reduction methods have different performances in dimensionality reduction, which can be measured by prediction accuracies of some common statistical models, such as Naive Bayes classifier, linear discriminant analysis, quadratic discriminant analysis, and classification and regression tree. Gear crack level degradation data collected from a machine in a laboratory under different operating conditions including four different motor speeds and three different loads are used to investigate performances of the linear and non-linear dimensionality reduction methods. In our case study, the results show that principal component analysis has the best performance in dimensionality reduction and it results in the highest prediction accuracies in all of the aforementioned statistical models. In other words, the linear dimensionality reduction method is better than all of the non-linear dimensionality reduction methods investigated in this paper.

Local Neighborhood Embedding for Unsupervised Nonlinear Dimension Reduction

The construction of similarity relationship amongdata points plays a critical role in manifold learning.There exist two popular schemes, i.e., pairwise-distancebased similarity and reconstruction coefficient based similarity.Existing works only have involved one scheme of them.These two schemes have different drawbacks. For pairwisedistancebased similarity graph algorithms, they are sensitiveto the noise and outliers. For reconstruction coefficient basedsimilarity graph algorithms, they need sufficient sampleddata and the neighborhood size is sensitive. This paperproposes a novel algorithm, called Local NeighborhoodEmbedding (LNE), which preserves pairwise-distance basedsimilarity and reconstruction coefficient based similarity forfinding the latent low dimensional structure of data. It hasfollowing three advantages: Firstly, it is insensitive to thechoice of neighborhood size; Secondly, it is robust to thenoise; Thirdly, It works well even in under-sampled case.Furthermore, the proposed objective function has a closedformsolution, which means it has a low computationalcomplexity, and the experimental results illustrate that LNEhas a competitive performance in dimensionality reduction.

Dimensionality reduction and classification based on lower rank tensor analysis for hyperspectral imagery

PDF HTML阅读 XML下载 导出引用 引用提醒 基于低秩张量分析的高光谱图像降维与分类 DOI: 作者: 作者单位: 复旦大学电子工程系,复旦大学电子工程系,复旦大学电子工程系 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（面上项目，重点项目，重大项目）;国家教育部博士点基金 Dimensionality reduction and classification based on lower rank tensor analysis for hyperspectral imagery Author: Affiliation: Department of Electronic Engineering,Fudan University,Department of Electronic Engineering,Fudan University,Department of Electronic Engineering,Fudan University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:提出一种用于高光谱图像降维和分类的分块低秩张量分析方法.该算法以提高分类精度为目标，对图像张量分块进行降维和分类.将高光谱图像分成若干子张量，不仅保存了高光谱图像的三维数据结构，利用了空间与光谱维度的关联性，还充分挖掘了图像局部的空间相关性.与现有的张量分析法相比，这种分块处理方法克服了图像的整体空间相关性较弱以及子空间维度的设定对降维效果的负面影响.只要子空间维度小于子张量维度，所提议的分块算法就能取得较好的降维效果，其分类精度远远高于不分块的算法，从而无需借助原本就不可靠的子空间维度估计法.仿真和真实数据的实验结果表明，所提议分块低秩张量分析算法明显地表现出较好的降维效果，具有较高的分类精度. Abstract:Sub-tensor based lower rank tensor analysis used for dimensionality reduction and classification in hyperspectral imagery is proposed in this paper. The method aims at raising classification accuracy by representing the hyperspectral image as a tensor, divides it into sub-tensors and performs dimensionality reduction and pixel classification in each sub-tensor. Owing to the idea of creating sub-tensors, the method capitalizes on local spatial correlation, exploits interaction between spatial and spectral dimensions, and maintains hyperspectral data structure with 3D tensor. Compared with existing theories based on tensor analysis, the proposed method eliminates the negative impacts of poor subspace dimension estimation and low global spatial correlation, which might seriously degrade performances of dimensionality reduction. Moreover, as long as subspace dimensions are smaller than sub-tensor dimensions, the method with sub-tensors achieves much higher classification accuracy than the method without sub-tensors. Therefore, for the proposed method, it is not necessary to estimate the subspace dimension. Finally, experimental results of both simulated and real hyperspectral data demonstrate that Sub-Tensor based Lower Rank Tensor Analysis gives better performance in dimensionality reduction and brings higher classification accuracy than existing methods do. 参考文献 相似文献 引证文献

Hyperspectral band clustering and band selection for urban land cover classification

The aim of this study is to combine band clustering with band selection for dimensionality reduction of hyperspectral imagery. The performance of dimensionality reduction is evaluated through urban land cover classification accuracy with the dimensionality-reduced data. Different from unsupervised clustering using all the pixels or supervised clustering requiring labelled pixels, the discussed semi-supervised band clustering needs class spectral signatures only; band selection result is used as initial condition for band clustering; after clustering, a cluster selection step is applied to select clusters to be used in the following data analysis. In this article, we propose to conduct band selection by removing outlier bands in each cluster before finalizing cluster centres. The experimental results in urban land cover classification show that the proposed algorithm can further enhance support vector machine (SVM)-based classification accuracy.