[Day37] PCA (주성분 분석)
주성분분석, Principal Component Analysis에 대해서 잘 모르시겠다면 아래 내용들을 살펴보세요.
1. 고유치 및 고유벡터
www.youtube.com/watch?v=V7a86NUYjhY&feature=youtu.be
2. 고유값 분해 (Eigenvalue Decomposition) 쉽게 설명하기
www.youtube.com/watch?v=j2Zxmt5SRmo&feature=youtu.be
3. 주성분분석(Principal Component Analysis)
ratsgo.github.io/machine%20learning/2017/04/24/PCA/
주성분분석(Principal Component Analysis) · ratsgo's blog
이번 글에서는 차원축소(dimensionality reduction)와 변수추출(feature extraction) 기법으로 널리 쓰이고 있는 주성분분석(Principal Component Analysis)에 대해 살펴보도록 하겠습니다. 이번 글은 고려대 강필성
ratsgo.github.io
4. 주성분분석(Principal Component Analysis, PCA) 유도하기
www.youtube.com/watch?v=_5Khg7NH0bs&feature=youtu.be
5. In Depth: Principal Component Analysis
jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html
In Depth: Principal Component Analysis | Python Data Science Handbook
Principal Component Analysis Summary¶ In this section we have discussed the use of principal component analysis for dimensionality reduction, for visualization of high-dimensional data, for noise filtering, and for feature selection within high-dimensiona
jakevdp.github.io