CLASSIFICATION-ORIENTED LOCALLY LINEAR EMBEDDING

The locally linear embedding (LLE) algorithm is hypothetically able to find a lower dimensional space than a linear method for preserving a data manifold originally embedded in a high dimensional space. However, uneven sampling over the manifold in real-world data ultimately causes LLE to suffer from the disconnected-neighborhood problem. Consequently, the final dimensionality required for the data manifold is multiplied by the number of disjoint groups in the complete data representation. In addition, LLE as an unsupervised method is unable to suppress between-class connections. This means that samples from different classes are mixed during reconstruction. This study presents CLLE, a classification-oriented LLE method that uses class label information from training samples to guide unsupervised LLE. The criterion for neighbor selection is redesigned using class-conditional likelihood as well as Euclidean distance. This algorithm largely eliminates fractured classes and lowers the incidence of connections between classes. Also, a reconnection technique is proposed as a supporting method for ensuring a fully connected neighborhood graph, so that CLLE is able to extract the fewest features. Experiments with simulated and real data show that CLLE exceeds the performance of linear methods. Comparable classification performance can be achieved by CLLE using fewer features. In comparison with LLE, CLLE demonstrates a higher aptitude for and flexibility towards classification.

Download Full-text

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/5490513 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Jiucheng Xu ◽

Huiyu Mu ◽

Yun Wang ◽

Fangzhou Huang

Keyword(s):

Correlation Coefficient ◽

Expression Profiles ◽

Feature Selection Method ◽

Rank Correlation ◽

Classification Performance ◽

Locally Linear Embedding ◽

Spearman’S Rank Correlation ◽

Spearman’S Rank Correlation Coefficient ◽

Linear Embedding ◽

Locally Linear

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman’s rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman’s rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

Download Full-text

Functional data representation using correntropy locally linear embedding

2010 IEEE International Workshop on Machine Learning for Signal Processing ◽

10.1109/mlsp.2010.5589195 ◽

2010 ◽

Cited By ~ 2

Author(s):

Genaro Daza-Santacoloma ◽

German Castellanos-Dominguez ◽

Jose C. Principe

Keyword(s):

Functional Data ◽

Data Representation ◽

Locally Linear Embedding ◽

Linear Embedding ◽

Locally Linear

Download Full-text

A nonlinear method for monitoring industrial process

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220959232 ◽

2020 ◽

pp. 014233122095923

Author(s):

Yuan Li ◽

Chengcheng Feng

Keyword(s):

Fault Detection ◽

Objective Function ◽

Dimensional Space ◽

Detection Methods ◽

High Dimensional ◽

Locally Linear Embedding ◽

Industrial Case Study ◽

Laplacian Eigenmaps ◽

Linear Embedding ◽

Locally Linear

Aiming at fault detection in industrial processes with nonlinear or high dimensions, a novel method based on locally linear embedding preserve neighborhood for fault detection is proposed in this paper. Locally linear embedding preserve neighborhood is a feature-mapping method that combines Locally linear embedding and Laplacian eigenmaps algorithms. First, two weight matrices are obtained by the Locally linear embedding and Laplacian eigenmaps, respectively. Subsequently, the two weight matrices are combined by a balance factor to obtain the objective function. Locally linear embedding preserve neighborhood method can effectively maintain the characteristics of data in high-dimensional space. The purpose of dimension reduction is to map the high-dimensional data to low-dimensional space by optimizing the objective function. Process monitoring is performed by constructing T2 and Q statistics. To demonstrate its effectiveness and superiority, the proposed locally linear embedding preserve neighborhood for fault detection method is tested under the Swiss Roll dataset and an industrial case study. Compared with traditional fault detection methods, the proposed method in this paper effectively improves the detection rate and reduces the false alarm rate.

Download Full-text

Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.49 ◽

2014 ◽

Vol 536-537 ◽

pp. 49-52

Author(s):

Xiang Wang ◽

Yuan Zheng

Keyword(s):

Fault Diagnosis ◽

Learning Algorithm ◽

Classification Performance ◽

Fault Classification ◽

Locally Linear Embedding ◽

Diagnosis Method ◽

Linear Embedding ◽

Low Dimensional ◽

Machinery Fault Diagnosis ◽

Locally Linear

Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

Download Full-text

AN IMPROVED LOCALLY LINEAR EMBEDDING FOR SPARSE DATA SETS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001411008786 ◽

2011 ◽

Vol 25 (05) ◽

pp. 763-775 ◽

Cited By ~ 1

Author(s):

YING WEN ◽

LIANGHUA HE

Keyword(s):

Correlation Matrix ◽

Sparse Data ◽

Locally Linear Embedding ◽

Local Geometry ◽

Data Sets ◽

Real World Data ◽

Position Information ◽

Linear Embedding ◽

Sparse Data Sets ◽

Locally Linear

Locally linear embedding is often invalid for sparse data sets because locally linear embedding simply takes the reconstruction weights obtained from the data space as the weights of the embedding space. This paper proposes an improved method for sparse data sets, a united locally linear embedding, to make the reconstruction more robust to sparse data sets. In the proposed method, the neighborhood correlation matrix presenting the position information of the points constructed from the embedding space is added to the correlation matrix in the original space, thus the reconstruction weights can be adjusted. As the reconstruction weights adjusted gradually, the position information of sparse points can also be changed continually and the local geometry of the data manifolds in the embedding space can be well preserved. Experimental results on both synthetic and real-world data show that the proposed approach is very robust against sparse data sets.

Download Full-text

Sorting 4DCT Images Using Locally Linear Embedding

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.150 ◽

2013 ◽

Vol 462-463 ◽

pp. 150-154

Author(s):

Zhao Hui Luo ◽

Zai Fang Xi

Keyword(s):

Respiratory Motion ◽

Dimensional Space ◽

Image Data ◽

Locally Linear Embedding ◽

Time Resolved ◽

One Dimensional ◽

A Value ◽

4D Computed Tomography ◽

Linear Embedding ◽

Locally Linear

Respiratory motion degrades anatomic position reproducibility, and result in significant errors in radiotherapy. 4D computed Tomography (4DCT) can characterize anatomy motion during breathing. Usually, the acquired 4DCT images sequences is out of order. How to rearrange the sequence, i.e. sort 4DCT images has been the focus of 4DCT. In this paper we propose a method based on locally linear embedding (LLE), to reconstruct time-resolved CT volumes. By mapping high dimensional image data with LLE into one dimensional space, each image is assigned a value, then 4DCT images is sorted according to the value to reconstruct a respiratory cycle. Experiments result shows that the method is feasible to sort 4 DCT images without using any external motion monitoring systems.

Download Full-text

Temporal Registration of Cardiac Multimodal Images Using Locally Linear Embedding Algorithm

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v8i4.7757 ◽

2021 ◽

Author(s):

Talayeh Ghodsizad ◽

Hamid Behnam ◽

Emad Fatemizadeh ◽

Taraneh Faghihi Langroudi ◽

Fariba Bayat

Keyword(s):

Dimensional Space ◽

Absolute Error ◽

Extraction Methods ◽

Diagnostic Methods ◽

Locally Linear Embedding ◽

Cardiac Volume ◽

Fusion Methods ◽

Linear Embedding ◽

Temporal Registration ◽

Locally Linear

Purpose: Multimodal Cardiac Image (MCI) registration is one of the evolving fields in the diagnostic methods of Cardiovascular Diseases (CVDs). Since the heart has nonlinear and dynamic behavior, Temporal Registration (TR) is the fundamental step for the spatial registration and fusion of MCIs to integrate the heart's anatomical and functional information into a single and more informative display. Therefore, in this study, a TR framework is proposed to align MCIs in the same cardiac phase. Materials and Methods: A manifold learning-based method is proposed for the TR of MCIs. The Euclidean distance among consecutive samples lying on the Locally Linear Embedding (LLE) of MCIs is computed. By considering cardiac volume pattern concepts from distance plots of LLEs, six cardiac phases (end-diastole, rapid-ejection, end-systole, rapid-filling, reduced-filling, and atrial-contraction) are temporally registered. Results: The validation of the proposed method proceeds by collecting the data of Computed Tomography Coronary Angiography (CTCA) and Transthoracic Echocardiography (TTE) from ten patients in four acquisition views. The Correlation Coefficient (CC) between the frame number resulted from the proposed method and manually selected by an expert is analyzed. Results show that the average CC between two resulted frame numbers is about 0.82±0.08 for six cardiac phases. Moreover, the maximum Mean Absolute Error (MAE) value of two slice extraction methods is about 0.17 for four acquisition views. Conclusion: By extracting the intrinsic parameters of MCIs, and finding the relationship among them in a lower-dimensional space, a fast, fully automatic, and user-independent framework for TR of MCIs is presented. The proposed method is more accurate compared to Electrocardiogram (ECG) signal labeling or time-series processing methods which can be helpful in different MCI fusion methods.

Download Full-text