DLPR: A Distributed Locality Preserving Dimension Reduction Algorithm

2012 ◽  
pp. 97-110
Author(s):  
Mina Ghashami ◽  
Hoda Mashayekhi ◽  
Jafar Habibi
Keyword(s):  
Dimension Reduction ◽  
Reduction Algorithm ◽  
Locality Preserving

Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment

2015 ◽  
Vol 42 (1) ◽  
pp. 306-324 ◽  
Author(s):  
Arman Khadjeh Nassirtoussi ◽  
Saeed Aghabozorgi ◽  
Teh Ying Wah ◽  
David Chek Ling Ngo
Keyword(s):  
Text Mining ◽  
Dimension Reduction ◽  
Reduction Algorithm ◽  
News Headlines

scholarly journals A novel dimension reduction algorithm based on weighted kernel principal analysis for gene expression data

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258326
Author(s):  
Wen Bo Liu ◽  
Sheng Nan Liang ◽  
Xi Wen Qin
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Random Forest ◽  
Dimension Reduction ◽  
Principal Component ◽  
Classification Performance ◽  
Kernel Functions ◽  
Reduction Algorithm ◽  
Expression Data ◽  
Weighted Kernel

Gene expression data has the characteristics of high dimensionality and a small sample size and contains a large number of redundant genes unrelated to a disease. The direct application of machine learning to classify this type of data will not only incur a great time cost but will also sometimes fail to improved classification performance. To counter this problem, this paper proposes a dimension-reduction algorithm based on weighted kernel principal component analysis (WKPCA), constructs kernel function weights according to kernel matrix eigenvalues, and combines multiple kernel functions to reduce the feature dimensions. To further improve the dimensional reduction efficiency of WKPCA, t-class kernel functions are constructed, and corresponding theoretical proofs are given. Moreover, the cumulative optimal performance rate is constructed to measure the overall performance of WKPCA combined with machine learning algorithms. Naive Bayes, K-nearest neighbour, random forest, iterative random forest and support vector machine approaches are used in classifiers to analyse 6 real gene expression dataset. Compared with the all-variable model, linear principal component dimension reduction and single kernel function dimension reduction, the results show that the classification performance of the 5 machine learning methods mentioned above can be improved effectively by WKPCA dimension reduction.

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