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The following is a proof that the objective function in the non-centered case is exactly equal to the objective function in the centered case. Recall that K-means finds clusterings by minimizing $\sum_{k=1}^K \sum_{y\in X_k}\|y-\mu_k\|_2^2$ over clusterings $\{X_k\}_{k=1}^K$, where $\mu_k$ is the centroid of $X_k$. Let $\{x_i\}_{i=1}^n\subset\mathbb{R}^D$ be ...


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Forcing unit variances might be better because the standard k-means approximates spherical Gaussian distributions around centroids and, as a consequence, might favor inflated features like you said. But this is not effective in all cases since there are also other factors implied: deeply correlated features, scaling is global over all clusters, and so on. ...


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It seems blending is mixing up outcomes from many models and resulting in a better result. Is there any resource that helps me knowing more about it? Indeed, this is how they work. They try to give an optimal weight to (or directly learn from) the outputs of other learners. They usually achieve state of the art performance (after careful tuning) over almost ...


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