Dimensionality reduction algorithms may be of great help as decision support, representing the information as a map which summarizes the data similarities. When data come with an assigned class label, such a map can be used to check the quality of the labeling detecting class outliers or data near decision boundary, or to evaluate the relevance of the similarity measure used for the mapping from which to derive a good classification space. However, state-of-the-art mapping techniques are either

more »

... unsupervised, not considering the class labels, or supervised, considering it but putting too much emphasis on the class information. The result is that well separated classes can be mapped as overlapping with the unsupervised techniques, while overlapping classes can be mapped as clearly separated with the supervised techniques, so none of these maps tends to show the truth about the inter-class and between-class high-dimensional structure. We designed ClassiMap, a supervised mapping technique which come over these limits by exploiting the unavoidable tears and false neighborhoods mapping distortions to preserve at best the class structure through the mapping. We compare it to other supervised mapping techniques in labeled data visual exploration tasks. Paper type: Optimizing embeddings for visual analysis