# Image Classification Using Singular Value Decomposition and Optimization

Source: [Image Classification Using Singular Value Decomposition and Optimization](<https://www.sota2.com/research/paper/68fa5cb11e209131ce9d36c5>)

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

Data updated: 2025-10-25T17:48:19.946Z

Authors: Isabela M. Yepes, Manasvi Goyal

Published: 2024-12-10T08:17:13.000Z

## Abstract

This study investigates the applicability of Singular Value Decomposition for the image classification of specific breeds of cats and dogs using fur color as the primary identifying feature. Sequential Quadratic Programming (SQP) is employed to construct optimally weighted templates. The proposed method achieves 69% accuracy using the Frobenius norm at rank 10. The results partially validate the assumption that dominant features, such as fur color, can be effectively captured through low-rank approximations. However, the accuracy suggests that additional features or methods may be required for more robust classification, highlighting the trade-off between simplicity and performance in resource-constrained environments.

## 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/2412.07288>)

[PDF](<https://arxiv.org/pdf/2412.07288.pdf>)