Research Article
Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor
Algorithm 1
Extracting sparse softmax vector of a testing sample.
| | Input: K-class subjects, training sample X and a testing sample y normalized with L2-norm, parameters , , , identity matrix I | | | Output: softmax vector of K-class | | (1) | initialize | | (2) | repeat | | (3) | update | | (4) | update | | (5) | update | | (6) | until convergence | | (7) | let | | (8) | for each k in {1, 2, 3, … , K} do | | (9) | let | | (10) | let | | (11) | end for | | (12) | let | | (13) | output |
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