Sparse Subspace Denoising for Image Manifolds

Bo Wang, Zhuowen Tu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 468-475

Abstract


With the increasing availability of high dimensional data and demand in sophisticated data analysis algorithms, manifold learning becomes a critical technique to perform dimensionality reduction, unraveling the intrinsic data structure. The real-world data however often come with noises and outliers; seldom, all the data live in a single linear subspace. Inspired by the recent advances in sparse subspace learning and diffusion-based approaches, we propose a new manifold denoising algorithm in which data neighborhoods are adaptively inferred via sparse subspace reconstruction; we then derive a new formulation to perform denoising to the original data. Experiments carried out on both toy and real applications demonstrate the effectiveness of our method; it is insensitive to parameter tuning and we show significant improvement over the competing algorithms.

Related Material


[pdf]
[bibtex]
@InProceedings{Wang_2013_CVPR,
author = {Wang, Bo and Tu, Zhuowen},
title = {Sparse Subspace Denoising for Image Manifolds},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}