# Compute Less to Get More: Using ORC to Improve Sparse Filtering

Source: [Compute Less to Get More: Using ORC to Improve Sparse Filtering](<https://www.sota2.com/research/paper/68fa61e21e209131ce9f6608>)

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

Data updated: 2025-10-27T05:49:55.019Z

Authors: Johannes Lederer, Sergio Guadarrama

Published: 2014-09-16T16:31:07.000Z

## Abstract

Sparse Filtering is a popular feature learning algorithm for image classification pipelines. In this paper, we connect the performance of Sparse Filtering with spectral properties of the corresponding feature matrices. This connection provides new insights into Sparse Filtering; in particular, it suggests early stopping of Sparse Filtering. We therefore introduce the Optimal Roundness Criterion (ORC), a novel stopping criterion for Sparse Filtering. We show that this stopping criterion is related with pre-processing procedures such as Statistical Whitening and demonstrate that it can make image classification with Sparse Filtering considerably faster and more accurate.

## 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/1409.4689>)

[PDF](<https://arxiv.org/pdf/1409.4689.pdf>)