Abstract
BackgroundPractitioners and researchers often found the intrinsic representations of high-dimensional problems has much fewer independent variables. However such intrinsic structure may not be easily discovered due to noises and other factors.A supervised transformation scheme RST is proposed to transform features into lower dimensional spaces for classification tasks. The proposed algorithm recursively and selectively transforms the features guided by the output variables.ResultsWe compared the classification performance of linear classifier and random forest classifier on the original data sets, data sets being transformed with RST and data sets being transformed by principle component analysis and linear discriminant analysis. On 7 out 8 data sets RST shows superior classification performance with linear classifiers but less ideal with random forest classifiers.ConclusionsOur test shows the proposed method’s capability to reduce features dimensions in general classification tasks and preserve useful information using linear transformations. Some limitations of this method are also pointed out.
Highlights
Practitioners and researchers often found the intrinsic representations of high-dimensional problems has much fewer independent variables
Transformations are learnt by Recursive Selective Feature Transformation (RST), linear discriminant analysis (LDA) [14] and Principle Component Analysis (PCA) on the training set, both of the training and testing set are transformed with the learnt transformer
The comparison of Random Forest and Support vector machines (SVM) performance on original datasets, linear discriminate analysis (LDA) transformed data sets, PCA transformed data sets and RST transformed data sets is in Tables 1 and 2
Summary
Practitioners and researchers often found the intrinsic representations of high-dimensional problems has much fewer independent variables. Such intrinsic structure may not be discovered due to noises and other factors. A supervised transformation scheme RST is proposed to transform features into lower dimensional spaces for classification tasks. The proposed algorithm recursively and selectively transforms the features guided by the output variables. In machine learning tasks the intrinsic representations of high-dimensional data may have much fewer independent variables, as suggested by Hastie [1] in hand written recognitions, the motion of objects [2], and array signal processing [3].
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