scholarly journals Semi-supervised optimization algorithm basedon Laplacian Eigenmaps

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Optimization Algorithm ◽  
Learning Algorithm ◽  
Space Structure ◽  
Experimental Results ◽  
Reduction Algorithm ◽  
Laplacian Eigenmaps ◽  
Original Space

As a member of many dimensionalityreduction algorithms, manifold learning is the hotspot ofrecent dimensionality reduction algorithm. Despite it isgood at retaining the original space structure, there is nodenying that its effect of classifying still has room forimprovement. Based on Laplacian Eigenmap, which is oneof the manifold learning algorithm, this paper committed tooptimize the algorithm combined with a semi-supervisedlearning ideas, which can improve the recognition rate.Finally, the better method of two forms is tested in thesurface electromyography system and plant leafidentification system. The experimental results show thatthis semi-supervised method does well in classifying.

Macro Manifold Learning with Applications to Supervised Classification

2014 ◽  
Vol 556-562 ◽  
pp. 3590-3593
Author(s):  
Hong Bing Huang
Keyword(s):  
Pattern Recognition ◽  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Supervised Classification ◽  
Experimental Results ◽  
Classification Tasks ◽  
Low Dimensional

Manifold learning has made many successful applications in the fields of dimensionality reduction and pattern recognition. However, when it is used for supervised classification, the result is still unsatisfactory. To address this challenge, a novel supervised approach, namely macro manifold learning (MML) is proposed. Based on the proposed approach, the low-dimensional embeddings of the testing samples is more favorable for classification tasks. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.

LTSA Algorithm for Dimension Reduction of Microarray Data

2013 ◽  
Vol 645 ◽  
pp. 192-195 ◽  
Author(s):  
Xiao Zhou Chen
Keyword(s):  
Dimensionality Reduction ◽  
Dimension Reduction ◽  
Manifold Learning ◽  
Microarray Data ◽  
Learning Algorithm ◽  
Medical Applications ◽  
Data Sets ◽  
Learning Method ◽  
Data Dimensionality Reduction ◽  
Low Dimensional

Dimension reduction is an important issue to understand microarray data. In this study, we proposed a efficient approach for dimensionality reduction of microarray data. Our method allows to apply the manifold learning algorithm to analyses dimensionality reduction of microarray data. The intra-/inter-category distances were used as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Colon cancer and leukaemia gene expression datasets are selected for our investigation. When the neighborhood parameter was effectivly set, all the intrinsic dimension numbers of data sets were low. Therefore, manifold learning is used to study microarray data in the low-dimensional projection space. Our results indicate that Manifold learning method possesses better effects than the linear methods in analysis of microarray data, which is suitable for clinical diagnosis and other medical applications.

scholarly journals Generalized EmbedSOM on quadtree-structured self-organizing maps

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 2120
Author(s):  
Miroslav Kratochvíl ◽  
Abhishek Koladiya ◽  
Jiří Vondrášek
Keyword(s):  
Data Analysis ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Manifold Learning ◽  
Generating Functions ◽  
The Self ◽  
Reduction Algorithm ◽  
Self Organizing Maps ◽  
Learning Techniques ◽  
Self Organizing

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

Classification Criterion Based Neighborhood Optimization Method on Laplacian Eigenmaps

2011 ◽  
Vol 403-408 ◽  
pp. 2679-2682
Author(s):  
Quan Sheng Jiang ◽  
Su Ping Li
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Parameter Optimization ◽  
Learning Algorithms ◽  
Optimization Method ◽  
Classification Performance ◽  
Nonlinear Dimensionality Reduction ◽  
Classification Criterion ◽  
Criterion Function ◽  
Laplacian Eigenmaps

Manifold learning algorithms are nonlinear dimensionality reduction algorithms rising in recent years. Laplacian Eigenmaps is a typical manifold learning algorithms. Aim to the difficulty of selecting neighborhood parameter on the algorithm, a neighborhood parameter optimization method based on classification criterion is proposed in the paper. From the point of the classification performance, the classification criterion function is constructed to reflect the distance of within-class and between-class. The optimization of the neighborhood is obtained according to the minimum of the criterion function. The experimental results on IRIS validate the optimization of the neighborhood and the effectiveness of feature classification.

scholarly journals Generalized EmbedSOM on quadtree-structured self-organizing maps

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 2120 ◽  
Author(s):  
Miroslav Kratochvíl ◽  
Abhishek Koladiya ◽  
Jiří Vondrášek
Keyword(s):  
Data Analysis ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Manifold Learning ◽  
Generating Functions ◽  
The Self ◽  
Reduction Algorithm ◽  
Self Organizing Maps ◽  
Learning Techniques ◽  
Self Organizing

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-based embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

New Measure Based Manifold Algorithm and Application in Anomaly Detection of Hyperspectral Imagery

2011 ◽  
Vol 80-81 ◽  
pp. 797-803
Author(s):  
Liang Liang Wang ◽  
Zhi Yong Li ◽  
Ji Xiang Sun ◽  
Chun Du
Keyword(s):  
Anomaly Detection ◽  
Manifold Learning ◽  
High Dimension ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Detection Performance ◽  
Hyperspectral Data ◽  
Experimental Results ◽  
Nonlinear Structure ◽  
Spectral Angle

Hyperspectral data is endowed with characteristics of intrinsic nonlinear structure and high dimension. In this paper, a nonlinear manifold learning algorithm - ISOMAP is applied to anomaly detection. Then an improved ISOMAP algorithm is developed based on the analysis of the inherent characteristics of hyperspectral imagery. The improved ISOMAP algorithm selects neighborhood according to a novel measure of combination of spectral gradient and spectral angle in order to make the algorithm more robust to the changes of light and terrain. Experimental results prove the effectiveness of the algorithm in improving the detection performance.

A New Supervised Manifold Learning Algorithm Based on MMC and LSE

2014 ◽  
Vol 39 (12) ◽  
pp. 2077-2089
Author(s):  
Min YUAN ◽  
Lei CHENG ◽  
Ran-Gang ZHU ◽  
Ying-Ke LEI
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm

Generalized incremental manifold learning algorithm based on local smoothness

2013 ◽  
Vol 32 (6) ◽  
pp. 1670-1673
Author(s):  
Xue-yan ZHOU ◽  
Jian-min HAN ◽  
Yu-bin ZHAN
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Local Smoothness
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