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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276
1
부
머신러닝
그림
8-6
저차원에서 항상 간단하지 않은 결정 경계
요약하면 모델을 훈련시키기 전에 훈련 세트의 차원을 감소시키면 훈련 속도는 빨라지지만 항
상 더 낫거나 간단한 솔루션이 되는 것은 아닙니다. 이는 전적으로 데이터셋에 달렸습니다.
이제 차원의 저주가 무엇인지, 특히 매니폴드 가정이 성립할 때 차원 축소 알고리즘이 어떻게
작동하는지 감을 잡았기 바랍니다. 이 장의 나머지 부분에서는 가장 널리 알려진 알고리즘 중
일부를 살펴보도록 하겠습니다.
8.3
PCA
주성분 분석
Principal
Component
Analysis
(
PCA
)은 가장 인기 있는 차원 축소 알고리즘입니다. 먼저
데이터에 가장 가까운 초평면
hyperplane
을 정의한 다음, 데이터를 이 평면에 투영시킵니다.
277
8
장
차원 축소
8.3.1
분산 보존
저차원의 초평면에 훈련 세트를 투영하기 전에 먼저 올바른 초평면을 선택해야 합니다. 예를 들
어 [그림
8
-
7
]의 왼쪽 그래프는 간단한
2D
데이터셋이 세 개의 축 (즉, 일차원 초평면 )과 함께
표현되어 있습니다. 오른쪽 그래프는 데이터셋이 각 축에 투영된 ...
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