Journal ArticleDOI
Parameter-less Auto-weighted multiple graph regularized Nonnegative Matrix Factorization for data representation
Reads0
Chats0
TLDR
In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.Abstract:
Recently, multiple graph regularizer based methods have shown promising performances in data representation However, the parameter choice of the regularizer is crucial to the performance of clustering and its optimal value changes for different real datasets To deal with this problem, we propose a novel method called Parameter-less Auto-weighted Multiple Graph regularized Nonnegative Matrix Factorization (PAMGNMF) in this paper PAMGNMF employs the linear combination of multiple simple graphs to approximate the manifold structure of data as previous methods do Moreover, the proposed method can automatically learn an optimal weight for each graph without introducing an additive parameter Therefore, the proposed PAMGNMF method is easily applied to practical problems Extensive experimental results on different real-world datasets have demonstrated that the proposed method achieves better performance than the state-of-the-art approachesread more
Citations
More filters
Journal ArticleDOI
Auto-weighted collective matrix factorization with graph dual regularization for multi-view clustering
Proceedings ArticleDOI
Hypergraph regularized NMF by L 2,1 -norm for Clustering and Com-abnormal Expression Genes Selection
TL;DR: A novel method called Robust Hypergraph regularized Non-negative Matrix Factorization (RHNMF) is proposed to solve the problem of intrinsic geometrical structure, noise, and outliers in gene expression data and has better performance than other state-of-the-art methods.
Journal ArticleDOI
A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network
TL;DR: A novel optimization framework to detect complexes from protein-protein interaction (PPI) network, named PLSMC, which can match known complexes with a higher accuracy than other methods and has high functional homogeneity.
Proceedings ArticleDOI
Discriminative layered nonnegative matrix factorization for speech separation
TL;DR: The DL-NMF is developed by extending the generative bases in L- NMF to the discriminative bases which are estimated according to a discriminatives criterion and the experiments on single-channel speech separation show the superiority of DL-NHF to NMF and L-NMf in terms of the SDR, SIR and SAR measures.
Journal ArticleDOI
Towards a probabilistic semi-supervised Kernel Minimum Squared Error algorithm
TL;DR: A Probabilistic Laplacian-regularized Kernel Minimum Squared Error algorithm (named PrLapKMSE), in which the probabilities of the unlabeled data belonging to different classes are adaptively generated.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Related Papers (5)
Learning the parts of objects by non-negative matrix factorization
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more