arXiv:1905.02845
공식 코드 · Papers with Code
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
원문 초록
Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
Chainer: a Next-Generation Open Source Framework for Deep Learning
ALPCAHUS: Subspace Clustering for Heteroscedastic Data
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
Orthrus: A Bimodal Learning Architecture for Malware Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.