Research Article
Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor
Algorithm 2
Three-layer feature extraction.
| | Input: Training sample X, and a testing sample y, and K-class subjects | | | Output: | | (1) | let | | (2) | dividing X into 4 subregions: X1, X2, X3, X4 | | (3) | dividing y into 4 subregions: Y1, Y2, Y3, Y4 | | (4) | for each i in {1, 2, 3, 4} do | | (5) | let | | (6) | end for | | (7) | let | | (8) | dividing X into 16 subregions: | | (9) | dividing X into 16 subregions: | | (10) | for each j in {1, 2, 3, … , 16} do | | (11) | let | | (12) | end for | | (13) | let | | (14) | output |
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