【論】Li,2006,Efficient and Robust Feature Extractio～

Haifeng Li, Tao Jiang, and Keshu Zhang
Efficient and robust feature extraction by maximum margin criterion
IEEE Trans. Neural Netw., vol.17, pp.157, Jan.2006.
[PDF][Web Site]

・特徴抽出法として、MMC (maximum margin criterion) を提案する。
・データ
1.Fisher's iris dataset
2.Vehicle dataset
3.ORL face dataset
4.Brain cancer gene expression dataset [Pomeroy]
・比較法
1.MMC
2.kernel MMC
3.LDA
4.LDA + PCA
5.kernel PCA

・問題点「However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem.」
・概要「In this paper, a simple, efficient, and stable method is proposed to calculate the most discriminant vectors and to avoid the small sample size problem based on a new feature extraction criterion, the maximum margin criterion (MMC). Geometrically, MMC maximizes the (average) margin between classes.」
・問題点「In practice, the proposed nonlinear feature extractor could be slow when the dataset is large. It is an interesting topic to develop a fast algorithm for the proposed nonlinear feature extractor.」

・工学系の論文なので、主な用途は画像処理。ついでにマイクロアレイデータを試してみました～ というノリ。数式はチンプンカンプン。