# Sufficient Component Analysis for Supervised Dimension Reduction

Source: [Sufficient Component Analysis for Supervised Dimension Reduction](<https://www.sota2.com/research/paper/68fa7d181e209131cea917e8>)

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

Data updated: 2025-10-27T07:06:31.762Z

Authors: Makoto Yamada, Gang Niu, Jun Takagi, Masashi Sugiyama

Published: 2011-03-25T15:35:16.000Z

## Abstract

The purpose of sufficient dimension reduction (SDR) is to find the low-dimensional subspace of input features that is sufficient for predicting output values. In this paper, we propose a novel distribution-free SDR method called sufficient component analysis (SCA), which is computationally more efficient than existing methods. In our method, a solution is computed by iteratively performing dependence estimation and maximization: Dependence estimation is analytically carried out by recently-proposed least-squares mutual information (LSMI), and dependence maximization is also analytically carried out by utilizing the Epanechnikov kernel. Through large-scale experiments on real-world image classification and audio tagging problems, the proposed method is shown to compare favorably with existing dimension reduction approaches.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1103.4998>)

[PDF](<https://arxiv.org/pdf/1103.4998.pdf>)