本文探讨了低秩表示(LRR)在高维数据处理中的应用，特别是其在降维及噪声鲁棒性方面的优势。通过比较传统PCA方法，强调了LRR能够有效揭示数据中的多重子空间结构，并利用数据矩阵作为字典进行表达的特点。同时，介绍了鲁棒主成分分析(RPCA)如何通过最小化迹范数来提高对外部噪声和异常值的抵抗力。
Low-rankness well expresses the low dimensional structure of high dimensional data. Instead of using PCA which is plagued by a well-known, well-documented sensitivity to outliers, recent methods minimize the trace-norm of data matrix.
RPCA:
Where D is the lowrank structure of X, and E is the error term. Using trace norm, it will robust to noise and outliers.
Face recognition:
Low-Rank Representation (LRR) is an effective method for exploring the multiple subspace structures of data. It use the data matrix as a dictionary.
Normal estimation
扫一扫
2万+
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
