대한 계수 값이 큰 얼굴들은 오른쪽으로 돌아가 있습니다. 앞서 말한 것처럼, 이와 같은 패턴을
추출하는 것은 소리, 유전자 표현, 텍스트 데이터처럼 덧붙이는 구조를 가진 데이터에 적합합
니다. 인위적인 데이터셋을 사용한 예를 통해서 이에 관해 자세히 살펴보겠습니다.
다음은 세 개의 서로 다른 입력으로부터 합성된 신호입니다(그림
3
-
18
).
In [40]:
S = mglearn.datasets.make_signals()
plt.gure(gsize=(6, 1))
plt.plot(S, '-')
plt.xlabel("시간")
plt.ylabel("신호")
그림
3-18
원본 신호
218
파이썬 라이브러리를 활용한 머신러닝(번역개정판)
불행히도 우리는 원본 신호는 볼 수 없고 이 세 개가 섞인 신호만 관찰할 수 있는 상황입니다.
그래서 합쳐진 신호를 분해해서 원본 신호를 복원해야 합니다. 이 신호를 여러 방법으로
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