单词向量列表上的尺寸降低
我有一组表示单词的向量，每个向量具有300个特征，这意味着每个向量有300个浮点。我的目标是将维度降低到50，以便我可以获得一些空间。
如何使用EG TensorFlow在此矢量集上应用维度降低？我找不到一种方法，实现等方法，该方法将向量列表作为输入并减少。
I have a set of vectors that represent words and each vector has 300 features meaning that there are 300 floats for each vector. My goal is to reduce to dimensionality i.e. to 50 so that I can gain some space.
How can apply a dimensionality reduction on this vector set using e.g. tensorflow? I couldn't find a method, an implementation etc. that takes a list of vectors as input and reduces it.
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您可能需要研究用于文本处理的卷积神经网络。一般而言，CNN以降低输入向量而闻名。它们通常用于图像分类，但也用于文本和句子分类。您正在寻找的是输入向量的嵌入。引用：
这是从这里开始的：
todataScience
另一个ressource：
AnalyticsVidhya
You might want to look into convolutional neural networks for text processing. CNNs in general are known for dimensionality reduction of the input vectors. They are usually used for image classification but also work on text and sentence classification. What you are looking for is the embedding of an input vector. Quote:
This is from here:
TowardsDataScience
Another ressource:
AnalyticsVidhya