# Dynamic Mode Decomposition based feature for Image Classification

Source: [Dynamic Mode Decomposition based feature for Image Classification](<https://www.sota2.com/research/paper/68fa66801e209131cea10b92>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:52:21.192Z

Authors: Rahul-Vigneswaran K, Sachin-Kumar S, Neethu Mohan, Soman KP

Published: 2019-10-08T03:09:42.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.03188>)

[PDF](<https://arxiv.org/pdf/1910.03188.pdf>)