Group Sparsity and Graph Regularized Semi-Nonnegative Matrix Factorization with Discriminability for Data Representation
نویسندگان
چکیده
منابع مشابه
Group Sparsity and Graph Regularized Semi-Nonnegative Matrix Factorization with Discriminability for Data Representation
Abstract: Semi-Nonnegative Matrix Factorization (Semi-NMF), as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted extensive attention. However, standard Semi-NMF still suffers from the following limitations. First of all, Semi-NMF fits data in a Euclidean space, which ignores the geometrical structu...
متن کاملEquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) methods have proved to be powerful across a wide range of real-world clustering applications. Integrating multiple types of measurements for the same objects/subjects allows us to gain a deeper understanding of the data and refine the clustering. We have developed a novel Graph-reguarized multiview NMF-based method for data integration called EquiNMF. The ...
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A recent challenge in data analysis for science and engineering is that data are often represented in a structured way. In particular, many data mining tasks have to deal with group-structured prior information, where features or data items are organized into groups. In this paper, we develop group sparsity regularization methods for nonnegative matrix factorization (NMF). NMF is an effective d...
متن کاملNorm and Hessian Regularized Non-Negative Matrix Factorization with Discriminability for Data Representation
Matrix factorization based methods have widely been used in data representation. Among them, Non-negative Matrix Factorization (NMF) is a promising technique owing to its psychological and physiological interpretation of spontaneously occurring data. On one hand, although traditional Laplacian regularization can enhance the performance of NMF, it still suffers from the problem of its weak extra...
متن کامل`2,1 Norm and Hessian Regularized Non-Negative Matrix Factorization with Discriminability for Data Representation
Matrix factorization based methods have widely been used in data representation. Among them, Non-negative Matrix Factorization (NMF) is a promising technique owing to its psychological and physiological interpretation of spontaneously occurring data. On one hand, although traditional Laplacian regularization can enhance the performance of NMF, it still suffers from the problem of its weak extra...
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ژورنال
عنوان ژورنال: Entropy
سال: 2017
ISSN: 1099-4300
DOI: 10.3390/e19120627