Journal ArticleDOI
Low-Rank Matrix Factorization With Adaptive Graph Regularizer
Gui-Fu Lu,Yong Wang,Jian Zou +2 more
Reads0
Chats0
TLDR
A novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR) is presented, which results in an automatically updated graph rather than a predefined one.Abstract:
In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.read more
Citations
More filters
Journal ArticleDOI
Low-rank representation with adaptive graph regularization
TL;DR: Experimental results show that the proposed graph learning method can significantly improve the clustering performance and a novel rank constraint is further introduced to the model, which encourages the learned graph to have very clear clustering structures.
Journal ArticleDOI
Non-Negative Matrix Factorization With Locality Constrained Adaptive Graph
TL;DR: A novel graph regularized NMF algorithm called NMF with locality constrained adaptive graph (NMF-LCAG) is proposed, which can achieve at least 1% ~ 3% accuracy improvement in most cases.
Journal ArticleDOI
Graph regularized Lp smooth non-negative matrix factorization for data representation
TL;DR: Experiments show that the proposed Graph regularized Lp smooth non-negative matrix factorization method outperforms related state-of-the-art methods.
Journal ArticleDOI
Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences
TL;DR: A novel method for GPD recorded in HVSs that separates the background from the gas plume via a low-rank and sparse decomposition and a novel fusion strategy is proposed to combine the information into a final detection result.
Journal ArticleDOI
Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification
TL;DR: In SSLRR, the LRR and spectral–spatial graph regularization are developed as the pixel-level constraints to remove the redundant and noise information in HSIs and superpixel constraints including data structure and relationship construction are further utilized to provide supervised feedback information to the subspace learning.
References
More filters
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
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.
Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.