Research Article
An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition
Algorithm 1
The working process of KNN.
| Require: input data matrix, ; the target value, ; the parameter of KNN, K; the number of training data, L. | | Ensure: the forecasting output (). | | Data Separation: | (1) | The training features: ; | (2) | The training labels: ; | (3) | The testing features: ; | (4) | The texting labels: ; | | Loop: | (5) | fordo | (6) | fordo | (7) | Calculate the distance between and by equation (1); | (8) | end for | (9) | Sort the distance from smallest to largest value (in ascending order); | (10) | Pick the top K vectors from the sorted collection as an index; | (11) | Set the forecasting the class label based on the most frequent class of processed index. | (12) | end for | | return |
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