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.

Feature Dimension Reduction and Graph Based Ranking Based Image Classification

2013 ◽  
Vol 380-384 ◽  
pp. 4035-4038 ◽  
Author(s):  
Nan Yao ◽  
Feng Qian ◽  
Zuo Lei Sun
Keyword(s):  
Dimensionality Reduction ◽  
Dimension Reduction ◽  
Image Classification ◽  
Learning Algorithm ◽  
Contextual Information ◽  
Original Data ◽  
Classification Problem ◽  
Image Features ◽  
Similarity Learning ◽  
Low Dimensional

Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.

scholarly journals Robust Semi-Supervised Manifold Learning Algorithm for Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Mingxia Chen ◽  
Jing Wang ◽  
Xueqing Li ◽  
Xiaolong Sun
Keyword(s):  
Manifold Learning ◽  
Optimization Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Data Sets ◽  
Regularization Term ◽  
Partially Labeled Data ◽  
Label Information ◽  
Low Dimensional ◽  
The Impact

In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML) to address this problem. The noisy levels of the labeled points are firstly predicted, and then a regularization term is constructed to reduce the impact of labeled points containing noise. A new robust semi-supervised optimization model is proposed by adding the regularization term to the traditional semi-supervised optimization model. Numerical experiments are given to show the improvement and efficiency of RSSML on noisy data sets.

Dimension Reduction of Local Manifold Learning Algorithm for Hyperspectral Image Classification

2013 ◽  
pp. 217-231
Author(s):  
Sheng Ding ◽  
Li Chen ◽  
Jun Li
Keyword(s):  
Dimension Reduction ◽  
Image Classification ◽  
Manifold Learning ◽  
Hyperspectral Image ◽  
Data Representation ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Learning Methods ◽  
Multiple Data ◽  
Low Dimensional

This chapter addresses the problems in hyperspectral image classification by the methods of local manifold learning methods. A manifold is a nonlinear low dimensional subspace that is supported by data samples. Manifolds can be exploited in developing robust feature extraction and classification methods. The manifold coordinates derived from local manifold learning (LLE, LE) methods for multiple data sets. With a proper selection of parameters and a sufficient number of features, the manifold learning methods using the k-nearest neighborhood classification results produced an efficient and accurate data representation that yields higher classification accuracies than linear dimension reduction (PCA) methods for hyperspectral image.

scholarly journals Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 434 ◽  
Author(s):  
Huilin Ge ◽  
Zhiyu Zhu ◽  
Kang Lou ◽  
Wei Wei ◽  
Runbang Liu ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Gaussian Distribution ◽  
Classification Accuracy ◽  
Infrared Image ◽  
Data Sets ◽  
Dimensional Manifold ◽  
Infrared Images ◽  
Leibler Divergence ◽  
Data Points ◽  
Low Dimensional

Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

scholarly journals Semi-supervised hash learning method with consistency-based dimensionality reduction

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881917
Author(s):  
Fang Lv ◽  
Yuliang Wei ◽  
Xixian Han ◽  
Bailing Wang
Keyword(s):  
Dimensionality Reduction ◽  
Nearest Neighbor ◽  
Critical Role ◽  
Computational Cost ◽  
High Volume ◽  
Nearest Neighbor Search ◽  
Semantic Features ◽  
Learning Method ◽  
Retrieval Performance ◽  
Low Dimensional

With the explosive growth of surveillance data, exact match queries become much more difficult for its high dimension and high volume. Owing to its good balance between the retrieval performance and the computational cost, hash learning technique is widely used in solving approximate nearest neighbor search problems. Dimensionality reduction plays a critical role in hash learning, as its target is to preserve the most original information into low-dimensional vectors. However, the existing dimensionality reduction methods neglect to unify diverse resources in original space when learning a downsized subspace. In this article, we propose a numeric and semantic consistency semi-supervised hash learning method, which unifies the numeric features and supervised semantic features into a low-dimensional subspace before hash encoding, and improves a multiple table hash method with complementary numeric local distribution structure. A consistency-based learning method, which confers the meaning of semantic to numeric features in dimensionality reduction, is presented. The experiments are conducted on two public datasets, that is, a web image NUS-WIDE and text dataset DBLP. Experimental results demonstrate that the semi-supervised hash learning method, with the consistency-based information subspace, is more effective in preserving useful information for hash encoding than state-of-the-art methods and achieves high-quality retrieval performance in multi-table context.

scholarly journals Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Dong Liang ◽  
Chen Qiao ◽  
Zongben Xu
Keyword(s):  
Manifold Learning ◽  
Computational Efficiency ◽  
New Method ◽  
Data Sets ◽  
Learning Approaches ◽  
Real World Data ◽  
Efficiency Property ◽  
Data Points ◽  
Low Dimensional ◽  
High Computational Efficiency

The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set of landmarks to embed all data points. On the other hand, EL-Isomap significantly extends the representational capability of L-Isomap and other global manifold learning approaches by utilizing only an available subset from the whole landmark set instead of all to embed each point. Particularly, compared with other manifold learning approaches, the data manifolds with intrinsic low-dimensional concave topologies and essential loops can be unwrapped by the new method more successfully, which are shown by simulation results on a series of synthetic and real-world data sets. Moreover, the accuracy, robustness, and computational complexity of EL-Isomap are analyzed in this paper, and the relation between EL-Isomap and L-Isomap is also discussed theoretically.

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.

scholarly journals SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianping Zhao ◽  
Na Wang ◽  
Haiyun Wang ◽  
Chunhou Zheng ◽  
Yansen Su
Keyword(s):  
Dimensionality Reduction ◽  
Data Visualization ◽  
State Of The Art ◽  
Dimensional Space ◽  
High Dimensional ◽  
Reduction Algorithm ◽  
Cell Clustering ◽  
Data Dimensionality Reduction ◽  
Single Cell Rna Sequencing ◽  
Low Dimensional

Dimensionality reduction of high-dimensional data is crucial for single-cell RNA sequencing (scRNA-seq) visualization and clustering. One prominent challenge in scRNA-seq studies comes from the dropout events, which lead to zero-inflated data. To address this issue, in this paper, we propose a scRNA-seq data dimensionality reduction algorithm based on a hierarchical autoencoder, termed SCDRHA. The proposed SCDRHA consists of two core modules, where the first module is a deep count autoencoder (DCA) that is used to denoise data, and the second module is a graph autoencoder that projects the data into a low-dimensional space. Experimental results demonstrate that SCDRHA has better performance than existing state-of-the-art algorithms on dimension reduction and noise reduction in five real scRNA-seq datasets. Besides, SCDRHA can also dramatically improve the performance of data visualization and cell clustering.

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