May 2018
Beginner to intermediate
672 pages
16h 54m
Korean
Content preview from 핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
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291
8
장
차원 축소
불행하게도 마지막 항의
m
2
때문에 이 알고리즘을 대량의 데이터셋에 적용하기는 어렵습니
다.
28
8.6
다른 차원 축소 기법
사이킷런은 다양한 차원 축소 기법을 제공합니다. 다음은 그중에서 가장 널리 사용되는 것들입
니다.
●
다차원 스케일링
Multidimensional
Scaling
(
MDS
)은 샘플 간의 거리를 보존하면서 차원을 축소합니다 (그림
8
-
13
).
●
Isomap
은 각 샘플을 가장 가까운 이웃과 연결하는 식으로 그래프를 만듭니다. 그런 다음 샘플 간의 지오데
식 거리
geodesic
distance
29
를 유지하면서 차원을 축소합니다.
●
t
-
SNE
t
-
Distributed
Stochastic
Neighbor
Embedding
는 비슷한 샘플은 가까이, 비슷하지 않은 샘플은 멀리 떨어지도록
하면서 차원을 축소합니다. 주로 시각화에 많이 사용되며 특히 고차원 공간에 있는 샘플의 군집을 시각화할
때 사용됩니다(예를 들면
MNIST
데이터셋을
2D
로 시각화할 때).
●
선형 판별 분석
Linear
Discriminant
Analysis
(
LDA
)은 사실 분류 알고리즘입니다. 하지만 훈련 과정에서 클래스 사이
를 가장 잘 구분하는 축을 학습합니다. 이 축은 데이터가 투영되는 초평면을 정의하는 데 사용할 수 있습니다.
이 알고리즘의 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.