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With the rapid accumulation of high dimensional data, dimensionality reduction plays a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pair wise constraints. In this setting, domain knowledge is given in the form of pair wise constraints, which specifies whether a pair of instances belong to the same class...
Spectral clustering (SC) has become one of the most popular clustering methods. Given an affinity matrix, SC explores its spectral-graph structure to partition data into disjoint meaningful groups. However, in many applications, there are multiple potentially useful features and thereby multiple affinity matrices. For applying spectral clustering to such cases, these affinity matrices must be aggregated...
Automatic object recognition plays a central role in numerous applications, such as image retrieval and robot navigation. A now classical strategy consists to compute a bag of features within a sliding window and to compare this bag with precomputed models. One main drawback of this approach is the use of an unstructured bag of features which do not allow to take into account relationships which may...
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