Yu Ishikawa created SPARK-3012: ---------------------------------- Summary: Standardized Distance Functions between two Vectors for MLlib Key: SPARK-3012 URL: https://issues.apache.org/jira/browse/SPARK-3012 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Yu Ishikawa Priority: Minor
Most of the clustering algorithms need distance functions between two Vectors. We should include the standardized distance function library in MLlib. I think that the standardized distance functions help us to implement more machine learning algorithms efficiently. h3. For example - Chebyshev Distance - Cosine Distance - Euclidean Distance - Mahalanobis Distance - Manhattan Distance - Minkowski Distance - SquaredEuclidean Distance - Tanimoto Distance - Weighted Distance - WeightedEuclidean Distance - WeightedManhattan Distance -- This message was sent by Atlassian JIRA (v6.2#6252) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org