Enhancing Relative Representations using Custom Weighted Mahalanobis Distance
Background Relative representations are a powerful tool in machine learning and data analysis, where data points are represented based on their distances or similarities to a set of reference points called anchors. Traditional methods often rely on similarity measures, such as cosine similarity, which are invariant under rotation and scaling but may not satisfy the properties of a metric space, particularly the triangle inequality. This limitation can hinder the effectiveness of certain algorithms that require a proper distance metric. ...