Question

I m trying to figure out a way I could represent a Facebook user as a vector. I decided to go with stacking the different attributes/parameters of the user into one big vector (i.e. age is a vector of size 100, where 100 is the maximum age you can have, if you are lets say 50, the first 50 values of the vector would be 1 just like a thermometer). I just can t figure out a way to represent the Facebook interests as a vector too, they are a collection of words and the space that represents all the words is huge, I can t go for a model like a bag of words or something similar. Does anyone know how I should proceed? I m still new to this, any reference would be highly appreciated.

在希望否决表决的情况下,我只想知道这个问题有什么错误,以便我能够改进措辞和背景。

成就

Answer 1

“权利”办法取决于你的学习算法是什么以及决定问题是什么。

然而,代表年龄作为单一数字特征而不是100个指标特征往往更好。这样一来,学习算法就不必学习这些百种特征(摇摇摇摇摇摇摇篮)之间的关系,而问题涉及的方面却少了99个,使事情更加美好。

为了模仿这些利益,你不妨首先采用一种极高的言辞模式,然后使用各种备选办法之一来降低层面:

a general dimensionality-reduction technique like PCA or smarter nonlinear ones, including Kernel PCA or various nonlinear approaches: see wikipedia s overview of dimensionality reduction and of specifically nonlinear techniques
pass it through a topic model and use the learned topic weights as your features; examples include LSA, LDA, HDP and many more

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