UPDATING STRATEGIES FOR DISTANCE BASED CLASSIFICATION MODEL WITH RECURSIVE LEAST SQUARES
<p>The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment <i>A</i> examines the possibilities of introducing new classes with Recursive...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/163/2022/isprs-annals-V-3-2022-163-2022.pdf |
| Summary: | <p>The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment <i>A</i> examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment <i>B</i> is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment <i>B</i> aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach maximum classification accuracy quickly.</p><p>The results show that the new self-learning MLM method can classify new classes with RLS update but with a cost of decreasing accuracy. With a larger amount of reference points, one class can be introduced with reasonable accuracy. The results of experiment <i>B</i> indicate that self-learning MLM can be trained with a few reference points, and the self-learning model quickly reaches accuracy results comparable with nearest-neighbour NN-MLM. It seems that the self-learning MLM could be a comparable machine learning method for the application of hyperspectral imaging and remote sensing.</p> |
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| ISSN: | 2194-9042 2194-9050 |