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Therefore, to improve the performance of RDBC, we make use of bias reduction technique like bootstrap to modify RDBC.
At this stage, the linear order reduction technique can be applied in a straightforward manner.
In this method, FDA is applied as an optimal linear dimensionality reduction technique, in terms of maximizing the separation between different populations.
As an optimal linear dimensionality reduction technique, in terms of maximizing the separation between different populations, FDA has been studied in detail in the pattern classification literature[27 29].
PCA is a linear dimensionality reduction technique that can effectively extract the fundamental structure of a dataset without any need for modeling of the data.
First we applied PCA to the Cytokine compendium to investigate if this linear dimensionality reduction technique can find meaningful clusters in a low-dimensional representation of the apoptosis signaling network.
t-SNE is a non-linear dimensionality reduction technique which maps the original sample to sample distances in the high-dimensional feature space.
Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set.
On the other hand, both PCA and LDA are linear feature reduction techniques.
We also suspect that many important patterns cannot be captured by linear dimensionality reduction techniques alone.
Compared to linear reduction techniques, NLPCA has many advantages.
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