Incremental Manifold Learning Algorithm Using PCA on Overlapping Local Neighborhoods for Dimensionality Reduction

2008 ◽  
pp. 406-415 ◽  
Author(s):  
Yubin Zhan ◽  
Jianping Yin ◽  
Guomin Zhang ◽  
En Zhu
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Learning Algorithm

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 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.

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

scholarly journals Plant Leaf Recognition through Local Discriminative Tangent Space Alignment

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chuanlei Zhang ◽  
Shanwen Zhang ◽  
Weidong Fang
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Tangent Space ◽  
Reduction Method ◽  
Plant Leaf ◽  
Local Patch ◽  
Dimensionality Reduction Method

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals The research of semi-supervised manifold learning algorithm based on markov property

2015 ◽  
Vol 45 (5) ◽  
pp. 703-712
Author(s):  
QingSong XU ◽  
Liang SHEN ◽  
DongSheng CAO ◽  
Xin HUANG
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Markov Property
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