[$k$-means]{}. (c) solution of [G-MM]{}. The white crosses indicate location of the cluster centers. Color codes match up to a permutation.[]{data-label="fig:kmeans_visualization"}](images/D31_ground-truth.pdf "fig:"){width="\textwidth"}

[0.32]{} ![Visualization of clustering solutions on the D31 dataset [@kmeans:D31] from identical initializations. Random partition initialization scheme is used. (a) color-coded ground-truth clusters. (b) solution of [$k$-means]{}. (c) solution of [G-MM]{}. The white crosses indicate location of the cluster centers. Color codes match up to a permutation.[]{data-label="fig:kmeans_visualization"}](images/D31_hard-EM.pdf "fig:"){width="\textwidth"}

[0.32]{} ![Visualization of clustering solutions on the D31 dataset [@kmeans:D31] from identical initializations. Random partition initialization scheme is used. (a) color-coded ground-truth clusters. (b) solution of [$k$-means]{}. (c) solution of [G-MM]{}. The white crosses indicate location of the cluster centers. Color codes match up to a permutation.[]{data-label="fig:kmeans_visualization"}](images/D31_generalized-MM.pdf "fig:"){width="\textwidth"}

[0.49]{} ![image](images/D31_different-progress-coeffs.png){height="2in"}

[0.49]{} ![image](images/cloud_different-progress-coeffs.png){height="2in"}

[Latent Structural SVM]{}for Image Classification and Object Detection
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We consider the problem of training an [LS-SVM]{}classifier on the mammals dataset  [@mammals]. The dataset contains images of six mammal categories with image-level annotation. Locations of the objects in these images are not provided, and therefore, treated as latent variables in the model. Specifically, let $x$ be an image and $y$ be a class label ($y \in \{1,\ldots,6\}$ in this case), and let $z$ be the latent location of 