Supervised Manifold Learning with Incremental Stochastic Embeddings
نویسنده
چکیده
In this paper, we introduce an incremental dimensionality reduction approach for labeled data. The algorithm incrementally samples in latent space and chooses a solution that minimizes the nearest neighbor classification error taking into account label information. We introduce and compare two optimization approaches to generate supervised embeddings, i.e., an incremental solution construction method and a re-embedding approach. Both methods have in common that the objective is to minimize the nearest neighbor classification error computed in the low-dimensional space. The resulting embedding is a surrogate of the high-dimensional labeled set. The set allows conclusions about the data set structure and can be used as preprocessing step for classification of labeled patterns.
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