Graph regularized discriminative non-negative matrix factorization for face recognition

Long, Xianzhong; Lu, Hongtao; Peng, Yong; Li, Wenbin

doi:10.1007/s11042-013-1572-z

Cited by 61 publications

(31 citation statements)

References 19 publications

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“…NMF can learn partsbased representation of the data and significantly improves interpretability as compared to SVD. The advantages of this parts-based representation have been observed in many realworld problems such as sound analysis (Helen and Virtanen 2005), face recognition (Long et al 2014), image annotation (Kalayeh et al 2014), visual tracking (Wu et al 2013), document clustering (Qin et al 2017), cancer clustering (Wang et al 2013) and DNA gene expression analysis (Gaujoux and Seoighe 2012).…”

Section: Non-negative Matrix Factorizationmentioning

confidence: 99%

Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization

Zhang

2019

CCF Trans. Pervasive Comp. Interact.

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Collaborative filtering usually suffers from limited performance due to a data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue through the transfer of useful knowledge from an auxiliary domain to a target domain. However, the situation becomes complicated when the source and target domain share partial knowledge with each other. Transferring the unshared part across domains will cause negative transfer and may degrade the prediction accuracy in the target domain. To address this issue, in this paper, we present a novel model that exploits the latent factors in the target domain against the negative transfer. First, we transfer rating patterns from the source domain to approximate and reconstruct the target rating matrix. Second, to be specific, we propose a partial-adaptation nonnegative matrix factorization method to correct the transfer learning result and restore latent factors in the target. The final experiments completed on real world datasets demonstrate that our proposed approach effectively addresses the negative transfer and significantly outperforms the state-of-art transfer-learning model.

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Section: Non-negative Matrix Factorizationmentioning

confidence: 99%

Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization

Zhang

2019

CCF Trans. Pervasive Comp. Interact.

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“…The most widely used dissimilarity measure function is the Frobenius norm: J.X; W H T / Dk X W H T k 2 F . NMF has been proven to be closely related to the K-means and spectral clustering methods and is widely used in the field of image analysis, text clustering and collaborative filtering [10][11][12][13][14]. Meanwhile, NMF-based method for community mining, which is a long-standing yet very difficult task in social network analysis [15], has also received a lot of attention.…”

Section: Nmf-based Community Miningmentioning

confidence: 99%

A topic community‐based method for friend recommendation in large‐scale online social networks

Fei

et al. 2016

Concurrency and Computation

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Online social networks (OSNs) have become more and more popular and have attracted a great many users. Friend recommendation, which is one of the important services in OSN, can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider either user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via Nonnegative Matrix Factorization (NMF). This method first applies joint NMF model to mine topic communities existing in OSN by combing link and content information. Then it computes user pairwise similarities and makes friends recommendation based on topic communities. Furthermore, this method can be implemented using the MapReduce distributed computing framework. Extensive experiments show that our proposed method not only has better recommendation performance than state-of-the-art methods but also has good scalability to deal with the problem of friend recommendation in large-sale OSNs. Moreover, the application case demonstrates that it can significantly improve friend recommendation service in the real world OSN.

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“…(9) guarantees that the learned parts-based representation can retain the intrinsic geometrical structure of data and discriminative information. In essence, MRNMF is a semisupervised learning algorithm of the NMF.…”

Section: Manifold Regularized Non-negative Matrixmentioning

confidence: 99%

“…Li et al 12 proposed a local NMF that learns spatially localized, part-based subspace representation for visual patterns. 9,14,[18][19][20][21][22] However, NMF is an unsupervised learning algorithm. GNMF constructs a nearest-neighbor graph to encode the geometrical information of the data space with the aim to find a parts-based representation space in which two data points are sufficiently close to each other, if they are connected in the graph.…”

Section: Introductionmentioning

confidence: 99%

Manifold regularized non-negative matrix factorization with label information

Zhang

Wang

et al. 2016

J. Electron. Imaging

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regularized non-negative matrix factorization with label information,"Abstract. Non-negative matrix factorization (NMF) as a popular technique for finding parts-based, linear representations of non-negative data has been successfully applied in a wide range of applications, such as feature learning, dictionary learning, and dimensionality reduction. However, both the local manifold regularization of data and the discriminative information of the available label have not been taken into account together in NMF. We propose a new semisupervised matrix decomposition method, called manifold regularized non-negative matrix factorization (MRNMF) with label information, which incorporates the manifold regularization and the label information into the NMF to improve the performance of NMF in clustering tasks. We encode the local geometrical structure of the data space by constructing a nearest neighbor graph and enhance the discriminative ability of different classes by effectively using the label information. Experimental comparisons with the state-ofthe-art methods on theCOIL20, PIE, Extended Yale B, and MNIST databases demonstrate the effectiveness of MRNMF. Li et al.: Manifold regularized non-negative matrix factorization with label information Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/16/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Graph regularized discriminative non-negative matrix factorization for face recognition

Cited by 61 publications

References 19 publications

Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization

Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization

A topic community‐based method for friend recommendation in large‐scale online social networks

Manifold regularized non-negative matrix factorization with label information

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