科技回声

Does this seem right?| Task | Distance Measure ||-------------------------------|-----------------------|| Document classification | Cosine Distance || Semantic search | Cosine Distance || Recommendation systems | Cosine Distance || Image recognition | Euclidean Distance (L2)|| Speech recognition | Euclidean Distance (L2)|| Handwriting analysis | Euclidean Distance (L2)|| Recommendation systems | Inner Product (Dot Product)|| Collaborative filtering | Inner Product (Dot Product)|| Matrix factorization | Inner Product (Dot Product)|| Image processing | L2-Squared Distance || Error detection and correction| Hamming Distance || DNA sequence comparison | Hamming Distance || Taxicab geometry | Manhattan Distance || Chessboard distance | Manhattan Distance |

Even ignoring vector magnitudes, wouldn't cosine distance as a measure of similarity only make sense if you're working with a convex set? That seems like it's far from a guarantee working in a high-dimensional space.

Which vector similarity metric should I use?

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Which vector similarity metric should I use?

2 条评论