A New Method Combining LDA and PLS for Dimension Reduction

<div><p>Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS) method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PLS, respectively. The LDA-PLS amends the projection direction of LDA by using the information of PLS, while ex-LDA-PLS is an extension of LDA-PLS by combining the result of LDA-PLS and LDA, making the result closer to the optimal direction by an adjusting parameter. Comparative studies are provided between the proposed methods and other traditional dimension reduction methods such as Principal component analysis (PCA), LDA and PLS-LDA on two data sets. Experimental results show that the proposed method can achieve better classification performance.</p></div

Recognition rates vs. number of components on PCA, LDA-PLS, ex-LDA-PLS and PLS-LDA in Gas dataset.

<p>Acc represent classification accuracy. Green lines with circle represent the results of PCA, blue lines with left triangle are LDA-PLS classification results, red lines with up triangleare the ex-LDA-PLS results, and black dot lines are the mean result of PLS-LDA.</p

Estimation of Lambda.

<p>Acc represent classification accuracy. Red line is the classification accuracy of ex-LDA-PLS algorithm when lambda from 0 to 1, blue line is the best lambda which can make the classification results reach maximum.</p

Two dimensional data set with the using of different algorithms.

<p>Black line with a square represents the direction of the LDA which coincides exactly with the direction of LDA-PLS when the latent variable is equal to 2 (the proof can be seen from Section 3.2). Red dotted lines of and are the boundary lines which can be correctly separate the two kinds of samples, and all lines between these boundaries can also do. Boundary classification and correspond to the direction of ex-LDA-PLS () and ex-LDA-PLS ().</p

Recognition rates vs. number of components on PCA, LDA-PLS, ex-LDA-PLS and PLS-LDA in Raman dataset.

<p>Acc represent classification accuracy. Green lines with circle represent the results of PCA, blue lines with left triangle are LDA-PLS classification results, red lines with up triangle are the ex-LDA-PLS results, and black dot lines are the mean result of PLS-LDA.</p

Classification Accuracy of Different Comparative Algorithms on Gas Dataset.

<p>Classification Accuracy of Different Comparative Algorithms on Gas Dataset.</p

Data Sets.

<p>Data Sets.</p

Block diagram of the proposed bioinformatics and system biology pipeline.

Block diagram of the proposed bioinformatics and system biology pipeline.</p

Gene-disease association analysis of DEGs of 4 cancerous risk factors with FI using OMIM and DisGNET databases.

Ellipse-shaped nodes represent risk factors, and round-shaped nodes represent DEGs. Deep-green color and Round-shaped indicates common genes between FI and four cancerous risk factors.</p

The top 20 signalling pathways from GO biological process enrichment analysis were showed by the bar diagram with p-values.

The top 20 signalling pathways from GO biological process enrichment analysis were showed by the bar diagram with p-values.</p