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Locality preserving projections (LPP) is a useful tool for learning the manifold of high dimensional data, which is a linear approximation of nonlinear Laplacian Eigenmap (LE). However, the original LPP algorithm is an unsupervised method that extracts features without any reference to the output information. In this paper, a supervised LPP (SLPP) framework is proposed for output-related feature extraction...
Restricted Boltzmann machines (RBMs) are widely used for data representation and feature learning in various machine learning tasks. The undirected structure of an RBM allows inference to be performed efficiently, because the latent variables are dependent on each other given the visible variables. However, we believe the correlations among latent variables are crucial for faithful data representation...
A new improved approximate dynamic programming for multi-document summarization is presented. Our proposed algorithm improves the state-of-art approximate dynamic programming algorithm for multi-document summarization in [1]. The improvement of our method is attributed to the adding search in the backward direction at each sequential step of the dynamic programming procedure. The experimental results...
Matrix factorization based techniques, such as nonnegative matrix factorization and concept factorization, have attracted great attention in dimensionality reduction and data clustering. Previous studies show that both of them yield impressive results on image processing and document clustering. However, both of them are essentially unsupervised methods and cannot incorporate label information. In...
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