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We propose robust density estimation in a low dimensional space for anomaly detection. The outline of the method is as follows: first a low dimensional representation of the original data is learnt. Then, a robust density mixture model is estimated in the learnt space. Finally, the likelihood of a data point given the model parameters is used to apply anomaly detection. An efficient way for adapting...
One of the challenges in automatic detection and classification of underwater targets in sonar imagery is variation of the target returns and features with respect to target aspect. This paper adopts a framework for target classification that offers local invariance properties with respect to target aspect. Sonar image snippets of a target type at nearby aspects are related to each other via geometric...
Manifold learning methods are useful for high dimensional data analysis. Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data. Typically, this process is computationally expensive and the produced embedding is limited to the training data. In many real life scenarios, the ability to produce embedding...
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