Similarity of concepts is not distance.
Natural Language Processing with word2vec, p.64, where I wrote
Apples and tomatoes are similar. Both are red. Apples and green apples are similar. Both are apples. However, green apples and tomatoes are not so similar.
This phenomenon does not satisfy the trigonometric inequality requirement of distance.
How to solve this problem
Instead of treating the distance Vector similarity between vectors as it is, the distance after collapsing the vectors on various axes is used as the similarity #Collapse axes
https://gyazo.com/6f605fd9a0082f691b3b93c575ccd69e
To thwart a vector in an axial direction means to ignore the difference in that axial direction.
Dimensionality reduction is an abstraction #abstraction #ignore features #ignore differences
I doubt that one axis of the vector created by the current word2vec represents a convenient attribute like "color difference".
word2vec creates vectors based solely on the information of what words appear around a word, so it is not possible to create vectors based on the information of what words appear around a word.
I think something similar is going on in the human brain.
One of the techniques used in Deep Learning is [Dropout
A method of randomly selecting a neuron and stopping its activity to allow it to learn
Doing this increases [generalization performance
Stop the activity of randomly selected neurons
= set the value represented by that neuron to 0
= Crush in the direction of a randomly selected axis
degree of similarity in concept is not [distance
Word Meaning
semantic similarity
People who leap from one story to the next = vector search engines?
Is suggestion a vector search? I think not.
Random dimensionality reduction and then similarity search.
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