2,080 research outputs found
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regularized NMF (GrNMF) is proposed to find a compact
representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In GNMF, an affinity graph is constructed
from the original data space to encode the geometrical information. In this
paper, we propose a novel idea which engages a Multiple Kernel Learning
approach into refining the graph structure that reflects the factorization of
the matrix and the new data space. The GrNMF is improved by utilizing the graph
refined by the kernel learning, and then a novel kernel learning method is
introduced under the GrNMF framework. Our approach shows encouraging results of
the proposed algorithm in comparison to the state-of-the-art clustering
algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible
writin
Robust Spectral Clustering via Sparse Representation
Clustering high-dimensional data has been a challenging problem in data mining and machining learning. Spectral clustering via sparse representation has been proposed for clustering high-dimensional data. A critical step in spectral clustering is to effectively construct a weight matrix by assessing the proximity between each pair of objects. While sparse representation proves its effectiveness for compressing high-dimensional signals, existing spectral clustering algorithms based on sparse representation use those sparse coefficients directly. We believe that the similarity measure exploiting more global information from the coefficient vectors will provide more truthful similarity among data objects. The intuition is that the sparse coefficient vectors corresponding to two similar objects are similar and those of two dissimilar objects are also dissimilar. In particular, we propose two approaches of weight matrix construction according to the similarity of the sparse coefficient vectors. Experimental results on several real-world high-dimensional data sets demonstrate that spectral clustering based on the proposed similarity matrices outperforms existing spectral clustering algorithms via sparse representation
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