Ma, Z;
Lu, X;
Xie, J;
Yang, Z;
Xue, J-H;
Tan, Z-H;
Xiao, B;
(2020)
On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables.
IEEE Transactions on Neural Networks and Learning Systems
10.1109/tnnls.2020.2978858.
(In press).
Preview |
Text
TNNLS-ZhanyuMa-comparison-2020-accepted.pdf - Accepted Version Download (799kB) | Preview |
Abstract
As a typical non-Gaussian vector variable, a neutral vector variable contains nonnegative elements only, and its l₁-norm equals one. In addition, its neutral properties make it significantly different from the commonly studied vector variables (e.g., the Gaussian vector variables). Due to the aforementioned properties, the conventionally applied linear transformation approaches [e.g., principal component analysis (PCA) and independent component analysis (ICA)] are not suitable for neutral vector variables, as PCA cannot transform a neutral vector variable, which is highly negatively correlated, into a set of mutually independent scalar variables and ICA cannot preserve the bounded property after transformation. In recent work, we proposed an efficient nonlinear transformation approach, i.e., the parallel nonlinear transformation (PNT), for decorrelating neutral vector variables. In this article, we extensively compare PNT with PCA and ICA through both theoretical analysis and experimental evaluations. The results of our investigations demonstrate the superiority of PNT for decorrelating the neutral vector variables.
| Type: | Article |
|---|---|
| Title: | On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tnnls.2020.2978858 |
| Publisher version: | https://doi.org/10.1109/tnnls.2020.2978858 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Decorrelation, neutral vector variable, neutrality, non-Gaussian, nonlinear transformation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094360 |
Archive Staff Only
 |
View Item |
