Dynamic Mode Decomposition based feature for Image Classification
This addresses the challenge of unlabeled data for researchers in image classification, though it appears incremental as it builds on existing methods like SVM and RKS.
The paper tackles the problem of high data requirements in machine learning by proposing a novel feature extraction method using Dynamic Mode Decomposition (DMD), achieving competitive results with Random Kitchen Sink on Imagenet data.
Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.