In nearest-neighbor un-embedding, how is the classification of a new input determined?

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By finding the closest embedded vector to the input based on signed distance

By averaging the embedded vectors of all classes

By selecting the class with the most members in the training set

By calculating the Euclidean distance to all training inputs

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In nearest‑neighbor un‑embedding, the new input first receives a vector representation from the embedding model. That vector is compared to all stored vectors from labeled training data, usually by Euclidean or cosine distance. The training example whose vector is closest to the new vector is identified as the nearest neighbor. The class label of that nearest neighbor is then assigned to the new input, so the classification is simply the label of the closest stored example. For instance, if a new sentence’s embedding is nearest to an embedding of a “sports” sentence in the database, the new sentence is classified as sports.

Detailed Explanation

The method looks for the embedded vector that is closest to the new input. Other options are incorrect because Some think averaging all class vectors gives the best match; A misconception is that the biggest class always wins.

Key Concepts

Nearest-neighbor classification

Vector embedding

Signed distance

Topic

Nearest-Neighbor Un-embedding

Difficulty

easy level question

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understand

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