Links table
Abstract and 1 introduction
2 introductory
3. Reconsidering normalization
3.1 Reconsidering the normalization
3.2 Reconsidering the current RBN
4 Rimani’s normalization of lying groups
5 Liebn on lying groups of SPD and 5.1 distorted lying groups from SPD
5.2 Liebn on SPD
6 experiments
6.1 Experimental results
7 conclusions, recognition, and references
The contents of the appendix
Symbols
B Kyes Basic in SPDNET and TSMNET
C statistical results for expanding in Liben
D Liebn as a natural generalization component for Euclidean Bn
The field momentum for the field to classify EEG
And BackProPagation is a matrix function
G details and experiences Liebn on the SPD terrifying
H preliminary experiences on spin matrix
I am evidence of Limas and theories in the main paper
Symbols
For better clarity, we summarize all the symbols used in this paper on the tab. 6.
B basic layers in SPDNET and TSMNET
SPDNET (Huang & Van Gool, 2017) is the most classic nerve network SPD. SPDNET simulates the intense traditional feeding network, which consists of three basic building blocks
Where the maximum (·) is to glorify the elements. BIMAP and REEIG mimics non -linear transformation and stimulation, while Logeig sets SPD prescriptions in the shadow area in the identity matrix for classification.
C statistical results for expanding in Liben
In this section, we will show the scaling effect (EQ. (14)) to the population. We will see that although the resulting population contrast is generally not composed, it becomes analytical under certain circumstances, such as the SPD under lem or LCM. As a result, equivalent. (14) It can be normalized and transformed the inherent gossip distribution.
The LEMMA above notes that when ∆ is fixed, Y also follows a GAMOD.
By Pro. C.3, we can get directly the following natural result.
Authors:
(1) Zeng Chen, University of Tinto;
(2) Yue Song, University of Tinto and author opposite;
(3) Yanmi Leo, University of Louisville;
(4) Niko Cuby, University of Trento.
