✦ LIBER ✦

Distance preserving linear feature selection

✍ Scribed by Jack Bryant; Guseman L.F. Jr.

Book ID: 103323436
Publisher: Elsevier Science
Year: 1979
Tongue: English
Weight: 479 KB
Volume: 11
Category: Article
ISSN: 0031-3203
DOI: 10.1016/0031-3203(79)90046-3

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Linear feature selection with applicatio

Linear feature selection with applications

✍ H.P. Decell Jr.; L.F. Guseman Jr. 📂 Article 📅 1979 🏛 Elsevier Science 🌐 English ⚖ 716 KB

Local and global structure preserving ba

Local and global structure preserving based feature selection

✍ Yazhou Ren; Guoji Zhang; Guoxian Yu; Xuan Li 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 540 KB

Locality preserving multimodal discrimin

Locality preserving multimodal discriminative learning for supervised feature selection

✍ Zhao Zhang; Ning Ye 📂 Article 📅 2010 🏛 Springer-Verlag 🌐 English ⚖ 795 KB

Correcting the Kullback–Leibler distance

Correcting the Kullback–Leibler distance for feature selection

✍ Frans M. Coetzee 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 183 KB

A frequent practice in feature selection is to maximize the Kullback-Leibler (K-L) distance between target classes. In this note we show that this common custom is frequently suboptimal, since it fails to take into account the fact that classification occurs using a finite number of samples. In clas

Invariant optimal feature selection: A d

Invariant optimal feature selection: A distance discriminant and feature ranking based solution

✍ Jianning Liang; Su Yang; Adam Winstanley 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 244 KB

Feature Selection with a Linear Dependen

Feature Selection with a Linear Dependence Measure

✍ Das, S.K. 📂 Article 📅 1971 🏛 IEEE 🌐 English ⚖ 923 KB