Improved LLE Algorithm Based on Supervision

It focuses on locally linear embedding algorithm into LLE proposed supervised locally linear embedding algorithm (SLLE). That supervised manifold learning algorithm, which introduced adjustable parameters to effectively use the classification information, so as to make the SLLE have a stronger effect for classification problems. Finally, through a series of experiments to fully illustrate the proposed improvement of the effectiveness of the algorithm, the proposed oversight of the manifold learning algorithm can more effectively enhance manifold learning algorithms for classification problems proficiency.

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Improved Isomap Algorithm Based on Supervision

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1896 ◽

2013 ◽

Vol 427-429 ◽

pp. 1896-1899 ◽

Cited By ~ 1

Author(s):

Zhong Yong Wu ◽

Li Li Gan

Keyword(s):

Manifold Learning ◽

Isometric Embedding ◽

Learning Algorithm ◽

Learning Algorithms ◽

Classification Problem ◽

Classification Problems ◽

Series Of Experiments ◽

Effective Use

It focuses Isomap isometric embedding algorithm is proposed to improve supervised isometric embedding algorithm (SIsomap). Both supervised manifold learning algorithm, using the introduction of adjustable parameters in the form of classes in the classification problem for the effective use of information, making the manifold learning algorithms for classification classification problems have a stronger effect. Finally, through a series of experiments to fully illustrate the proposed improvement of the effectiveness of the algorithm, the proposed oversight of the manifold learning algorithm can more effectively enhance manifold learning algorithms for classification problems

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An adaptive locally linear embedding manifold learning approach for hyperspectral target detection

10.1117/12.2177466 ◽

2015 ◽

Cited By ~ 5

Author(s):

Amanda K. Ziemann ◽

David W. Messinger

Keyword(s):

Target Detection ◽

Manifold Learning ◽

Locally Linear Embedding ◽

Learning Approach ◽

Linear Embedding ◽

Locally Linear

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Improved Algorithm Base on Locally Linear Embedding

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2160 ◽

2014 ◽

Vol 644-650 ◽

pp. 2160-2163 ◽

Cited By ~ 1

Author(s):

Shi Min Liu ◽

Yan Ni Deng ◽

Yuan Xing Lv

Keyword(s):

Learning Algorithm ◽

Recognition Rate ◽

Feature Space ◽

Locally Linear Embedding ◽

Dispersion Matrix ◽

Redundant Information ◽

Training Samples ◽

Linear Embedding ◽

Low Dimensional ◽

Locally Linear

Locally linear embedding algorithm (LLE) , It makes up the shortcomings that the manifold learning algorithm can be only applied to training samples but not be extended to test samples . However, due to the presence of its Low-dimensional feature space redundant information,and its sample category information does not integrate into a low-dimensional embedding. For this shortcoming, here we introduce the two improved algorithms:the local linear maximum dispersion matrix algorithm (FSLLE) and the adaptive algorithm (ALLE), and the combinations of the above two algorithms.With this experience,combined Garbol and locally linear embedding algorithm (LLE) to compare each conclusion. The results proved to be effective elimination of redundant information among basis vectors and improve the recognition rate.

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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.

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Visualization and synthesis of data using manifold learning based on Locally Linear Embedding

2011 6th Colombian Computing Congress (CCC) ◽

10.1109/colomcc.2011.5936326 ◽

2011 ◽

Author(s):

Andres M. Alvarez-Meza ◽

Juliana Valencia-Aguirre ◽

Genaro Daza-Santacoloma ◽

Carlos D. Acosta-Medina ◽

German Castellanos-Dominguez

Keyword(s):

Manifold Learning ◽

Locally Linear Embedding ◽

Linear Embedding ◽

Locally Linear

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Regularizing extreme learning machine by dual locally linear embedding manifold learning for training multi-label neural network classifiers

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2020.104062 ◽

2021 ◽

Vol 97 ◽

pp. 104062

Author(s):

Mohammad Rezaei-Ravari ◽

Mahdi Eftekhari ◽

Farid Saberi-Movahed

Keyword(s):

Neural Network ◽

Extreme Learning Machine ◽

Manifold Learning ◽

Locally Linear Embedding ◽

Linear Embedding ◽

Learning Machine ◽

Neural Network Classifiers ◽

Locally Linear

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FSLLE: A Fast K Selection Algorithm for Locally Linear Embedding

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026818500037 ◽

2018 ◽

Vol 17 (01) ◽

pp. 1850003

Author(s):

Jin-Hang Liu ◽

Tao Peng ◽

Xiaogang Zhao ◽

Kunfang Song ◽

Minghua Jiang ◽

...

Keyword(s):

Face Recognition ◽

Spatial Correlation ◽

Manifold Learning ◽

High Dimensional ◽

Locally Linear Embedding ◽

Dimensional Manifold ◽

Correlation Index ◽

Recognition Algorithms ◽

Linear Embedding ◽

Locally Linear

Data in a high-dimensional data space may reside in a low-dimensional manifold embedded within the high-dimensional space. Manifold learning discovers intrinsic manifold data structures to facilitate dimensionality reductions. We propose a novel manifold learning technique called fast [Formula: see text] selection for locally linear embedding or FSLLE, which judiciously chooses an appropriate number (i.e., parameter [Formula: see text]) of neighboring points where the local geometric properties are maintained by the locally linear embedding (LLE) criterion. To measure the spatial distribution of a group of neighboring points, FSLLE relies on relative variance and mean difference to form a spatial correlation index characterizing the neighbors’ data distribution. The goal of FSLLE is to quickly identify the optimal value of parameter [Formula: see text], which aims at minimizing the spatial correlation index. FSLLE optimizes parameter [Formula: see text] by making use of the spatial correlation index to discover intrinsic structures of a data point’s neighbors. After implementing FSLLE, we conduct extensive experiments to validate the correctness and evaluate the performance of FSLLE. Our experimental results show that FSLLE outperforms the existing solutions (i.e., LLE and ISOMAP) in manifold learning and dimension reduction. We apply FSLLE to face recognition in which FSLLE achieves higher accuracy than the state-of-the-art face recognition algorithms. FSLLE is superior to the face recognition algorithms, because FSLLE makes a good tradeoff between classification precision and performance.

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Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

Sensors ◽

10.3390/s20174834 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4834

Author(s):

Jersson X. Leon-Medina ◽

Maribel Anaya ◽

Francesc Pozo ◽

Diego Tibaduiza

Keyword(s):

Feature Extraction ◽

Manifold Learning ◽

Classification Accuracy ◽

Learning Algorithm ◽

Learning Algorithms ◽

Electronic Tongue ◽

Locally Linear Embedding ◽

Data Set ◽

Nonlinear Feature Extraction ◽

Nonlinear Feature

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

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Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG

Journal of Sensors ◽

10.1155/2016/7481946 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Mingai Li ◽

Xinyong Luo ◽

Jinfu Yang ◽

Yanjun Sun

Keyword(s):

Feature Extraction ◽

Manifold Learning ◽

Locally Linear Embedding ◽

Full Account ◽

Time Frequency ◽

Rehabilitation System ◽

Nonlinear Features ◽

Linear Embedding ◽

Nonlinear Nature ◽

Locally Linear

Robotic-assisted rehabilitation system based on Brain-Computer Interface (BCI) is an applicable solution for stroke survivors with a poorly functioning hemiparetic arm. The key technique for rehabilitation system is the feature extraction of Motor Imagery Electroencephalography (MI-EEG), which is a nonlinear time-varying and nonstationary signal with remarkable time-frequency characteristic. Though a few people have made efforts to explore the nonlinear nature from the perspective of manifold learning, they hardly take into full account both time-frequency feature and nonlinear nature. In this paper, a novel feature extraction method is proposed based on the Locally Linear Embedding (LLE) algorithm and DWT. The multiscale multiresolution analysis is implemented for MI-EEG by DWT. LLE is applied to the approximation components to extract the nonlinear features, and the statistics of the detail components are calculated to obtain the time-frequency features. Then, the two features are combined serially. A backpropagation neural network is optimized by genetic algorithm and employed as a classifier to evaluate the effectiveness of the proposed method. The experiment results of 10-fold cross validation on a public BCI Competition dataset show that the nonlinear features visually display obvious clustering distribution and the fused features improve the classification accuracy and stability. This paper successfully achieves application of manifold learning in BCI.

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