April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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3.11
辞書学習
89
図 3-14 t-SNE による観測点の分離
3.10
他の次元削減手法
ここまでに、線形次元削減手法と非線形次元削減手法を見てきた。次に、幾何や距離尺度にまった
く依存しない手法を見てみよう。
3.11
辞書学習
辞書学習（
dictionary learning
）は元データのスパースな表現を学習する手法だ。結果として得られ
る行列は辞書（
dictionary
）と呼ばれ、辞書内のベクトルはアトム（
atom
）と呼ばれる。元データの点は、
このアトムの重み付き和で再構成される。
元データの特徴量数を
d
と し、 辞 書 内 の ア ト ム の 数 を
n
と す る。
n
＜
d
の場合を未完備
（
undercomplete
）な 辞 書 と 言 い 、
n
＞
d
の場合を過完備（
overcomplete
）な辞書と言う。未完備な辞書
を用いると次元削減が実現でき、元のデータを少量のベクトルで表現することになる。ここではこちら
に着目する
＊
1
。
辞書学習にはミニバッチで学習を行うものもある。ここでは
MNIST
数字データセットに対してこれ
を使う。他の次元削減手法と同様に、成分の数を指定する。バッチサイズと学習を行う回数も指定する。
ここでは
2
次元散布図で可視化したいので、非常に密な辞書を学習する。実際に用いる場合にはもっ
とスパースなものを使うことになるだろう。
# Mini-batch dictionary learning
from sklearn.decomposition import MiniBatchDictionaryLearning ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.