cosvw
Cosvw, short for cosine-weighted vector similarity, is a method used to quantify similarity between high-dimensional feature vectors by integrating the standard cosine similarity with a per-feature variance-based weighting scheme.
In this approach, each feature i is assigned a weight w_i derived from the variance var_i of
Origins and context: The method arises from efforts to improve robustness of similarity measures in noisy domains
Typical applications include content-based image retrieval, document similarity, and recommender systems. Benefits include increased robustness to
Implementation notes: compute variances from a representative corpus, apply a small epsilon to avoid division by