The Wavelet method is used  for feature extraction and dimensionality
reduction of microarray data.
Although the detection accuracy can be improved by increasing 2D Gabor filters with different parameters, the feature dimensionality
will be increased and the time consuming become large.
In the third section, we explain the dimensionality
reduction technique t-SNE in detail and describe how the Random Forest based dissimilarity measure is computed and incorporated in this method.
Singular value decomposition is a popular dimensionality
reduction method, through which one can get a projection: f : X [right arrow] [R.
Comparing the performance of the classifiers before and after the dimensionality
reduction used the paired T-test.
The hubness phenomenon  has recently come into focus as an important aspect of the curse of dimensionality
that affects many instance-based machine learning systems.
This data management text from Lu, Plataniotis, and Venetsanopoulos focuses on efficient dimensionality
reduction of high-dimensional data sets with acceptable fidelity.
Inspired by above analyses, a dimensionality
reduction algorithm called Tensor Graph-based Linear Discriminant Analysis (TGbLDA) is proposed in the paper.
1(c) can well exhibit the double-hump characteristic, and the dimensionality
of 1D range cut is much lower than the original image, so people usually take 1D range cut as the feature vector.
Transformation techniques that are usually applied for dimensionality
reduction include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Nonlinear Discriminant Analysis (NLDA) .
The first challenge is to find the optimum dimensionality
reduction technique that produces meaningful visualization.
The future of post-human geometry; a preface to a new theory of infinity, symmetry, and dimensionality