Difference between r1.13 and the current

@@ -1,6 +1,12 @@

#noindex
많은 양의 데이터를 데이터를 본질을 해치지 않는 저차원으로 압축하는 것
Method for compressing a lot of data into something that captures the essence of the original data

차원축소 [[dimensionality_reduction]],

특징추출 [[feature_extraction]] // [[특징,feature]]을 ...

기법.

[[분산,variance]]은 최대한 보존하면서

[[축,axis]] or [[기저,basis]]을 새로 찾는...

한국어 설명:
https://angeloyeo.github.io/2019/07/27/PCA.html

@@ -10,18 +16,35 @@

[[고유값,eigenvalue]]
[[고유벡터,eigenvector]]
[[공분산행렬,covariance_matrix]] curr see [[공분산,covariance]]

Kosambi–Karhunen–Loève theorem ... or

Karhunen–Loève theorem .... KKL_theorem KL_theorem ?

{

'''Kosambi–Karhunen–Loève theorem'''

//wpen

[[확률과정,stochastic_process]]론의 [[정리,theorem]].

확률과정을 [[직교함수,orthogonal_function]]들의 무한한 [[선형결합,linear_combination]]으로 [[표현,representation]]하는 것에 대한 -

analogous to: [[함수,function]]을 bounded_interval(유계구간? curr see [[구간,interval]]) [[푸리에_급수,Fourier_series]] ddddddddd

[[WpEn:Kosambi–Karhunen–Loève_theorem]]

Naver:"kkl 정리" Ggl:"kkl 정리" Google:kkl.theorem

Up: [[정리,theorem]]

}

= tmp links ko =
http://t-robotics.blogspot.com/2018/02/32-pca.html

https://www.samsungsds.com/kr/insights/mathematics_for_ML.html 65% 쯤. "주성분 분석에서의 응용" 검색.

= tmp links en =
Principal Component Analysis Explained Visually
https://setosa.io/ev/principal-component-analysis/
https://news.ycombinator.com/item?id=27017675

= ~~Twins~~ =

~~[[wiki:WpKo:주성분_분석]]~~

= tmp =

tmp video{

tmp video

{

StatQuest: Principal Component Analysis 주성분 분석 (PCA)
https://www.youtube.com/watch?v=FgakZw6K1QQ

@@ -31,17 +54,15 @@

Compare:
[[독립성분분석,independent_component_analysis,ICA]]

{

~~한국어 설명~~

~~https://angeloyeo.github.io/2020/07/14/ICA.html~~

~~에 의하면 [[중심극한정리,central_limit_theorem,CLT]]를 반대로 한 개념이라고.~~

}

[[주성분,principal_component]] { Up: [[성분,component]] ? }

----

Zeta:주성분_분석_PCA

[[Zeta:주성분_분석_PCA]]

[[WpKo:주성분_분석]]

[[WpEn:Principal_component_analysis]]

Up: [[자료,data]]??
[[분석,analysis]] > [[다변량분석,multivariate_analysis]]

[[성분분석,component_analysis]]? ( [[성분,component]]? ) (이건 아마 안 만들어질 듯.)

많은 양의 데이터를 데이터를 본질을 해치지 않는 저차원으로 압축하는 것
Method for compressing a lot of data into something that captures the essence of the original data

차원축소 dimensionality_reduction,
특징추출 feature_extraction // 특징,feature을 ...
기법.
분산,variance은 최대한 보존하면서
축,axis or 기저,basis을 새로 찾는...

한국어 설명:
https://angeloyeo.github.io/2019/07/27/PCA.html
https://darkpgmr.tistory.com/110

mklink
고유값,eigenvalue
고유벡터,eigenvector
공분산행렬,covariance_matrix curr see 공분산,covariance
Kosambi–Karhunen–Loève theorem ... or
Karhunen–Loève theorem .... KKL_theorem KL_theorem ?
{
Kosambi–Karhunen–Loève theorem