Feature selection based dual-graph sparse non-negative matrix factorization for local discriminative clustering

Yue, Meng; Shang, Ronghua; Jiao, Licheng; Zhang, Wenya; Yuan, Yijing; Yang, Shuyuan

doi:10.1016/j.neucom.2018.02.044

Cited by 70 publications

(16 citation statements)

References 23 publications

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“…For the samples and/or features, an affinity graph is constructed which models their local geometric properties during the process of selecting features. Recently, many new and efficient feature selection methods have been founded on manifold learning and dual-manifold learning including SGFS [17], GLOPSL [58], RGNMF [67], DSNMF [68], DSRMR [69], DGRCFR [70], DGSPSFS [71], DMvNMF [72], LRLMR [73], SLSDR [18], MRC-DNN [74], RML-RBF-DM [75], and EGCFS [76].…”

Section: Manifold Learningmentioning

confidence: 99%

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Saberi-Movahed

Mohammadifard

Mehrpooya

et al. 2021

Preprint

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One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

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Section: Manifold Learningmentioning

confidence: 99%

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Saberi-Movahed

Mohammadifard

Mehrpooya

et al. 2021

Preprint

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“…We conduct simulations to examine our RFA-LCF for data clustering and representation. The results of our RFA-LCF are compared with those of 12 related algorithms, i.e., NMF [4], PNMF [6], GNMF [7], DNMF [41], DSNMF [49], PAMGNMF [50], CF [5], LCCF [9], LCF [10], LGCF [48], GRLCF [26] and GCF [40], which are all closely related to our algorithm. Note that there are no parameters in NMF, PNMF and CF, and the hyperparameters of GNMF, DNMF, DSNMF, LCF, LCCF, GRLCF, LGCF, PAMGNMF and GCF are carefully chosen for fair comparison.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Then, the cluster label of each sample can be obtained from the linear coefficients. Due to the nonnegative constraint based additive reconstruction, NMF and its variants, e.g., Projective NMF (PNMF) [6], Graph Regularized NMF (GNMF) [7], Constrained NMF (CNMF) [8], Graph Dual Regularization NMF (DNMF) [41], Parameter-less Auto-weighted Multiple Graph regularized NMF (PAMGNMF) [50] and Dual-graph Sparse NMF (DSNMF) [49] are widely applied for characterizing and clustering the faces, documents and texts [45][46][47], etc. Although the enhanced results have been obtained, NMF and its variants still cannot handle data in the reproduc-ing kernel Hilbert space.…”

Section: Introductionmentioning

confidence: 99%

Flexible Auto-Weighted Local-Coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering

Zhang

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework is proposed for clustering high-dimensional data. Specifically, RFA-LCF integrates the robust flexible CF by clean data space recovery, robust sparse local-coordinate coding and adaptive weighting into a unified model. RFA-LCF improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a sparse projection to recover the underlying clean data space, and then the flexible CF is performed in the projected feature space. RFA-LCF also uses a L 2,1 -norm based flexible residue to encode the mismatch between the recovered data and its reconstruction, and uses the robust sparse localcoordinate coding to represent data using a few nearby basis concepts. For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates. By updating the local-coordinate preserving data, basis concepts and new coordinates alternately, the representation abilities can be potentially improved. Extensive results on public databases show that RFA-LCF delivers the state-of-the-art clustering results compared with other related methods.

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“…We conduct simulations on six public real image databases to examine RFA-LCF for data clustering and representation. The results of our RFA-LCF are compared with those of 12 related nonnegative factorization algorithms, i.e., NMF [8], PNMF [28], GNMF [2], DNMF [18], DSNMF [13], PAMGNMF [19], CF [24], LCCF [3], LCF [11], LGCF [9], GRLCF [27] and GCF [26], which are closely related to our RFA-LCF. The information of evaluated datasets are shown in Table I.…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust Unsupervised Flexible Auto-weighted Local-coordinate Concept Factorization for Image Clustering

Zhang¹,

Zhang²,

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We investigate the high-dimensional data clustering problem by proposing a novel and unsupervised representation learning model called Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF). RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model. The adaptive weighting is driven by including the joint manifold preserving constraints on the recovered clean data, basis concepts and new representation. Specifically, our RFA-LCF uses a L2,1-norm based flexible residue to encode the mismatch between clean data and its reconstruction, and also applies the robust adaptive sparse local-coordinate coding to represent the data using a few nearby basis concepts, which can make the factorization more accurate and robust to noise. The robust flexible factorization is also performed in the recovered clean data space for enhancing representations. RFA-LCF also considers preserving the local manifold structures of clean data space, basis concept space and the new coordinate space jointly in an adaptive manner way. Extensive comparisons show that RFA-LCF can deliver enhanced clustering results.

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Feature selection based dual-graph sparse non-negative matrix factorization for local discriminative clustering

Cited by 70 publications

References 23 publications

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Flexible Auto-Weighted Local-Coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering

Robust Unsupervised Flexible Auto-weighted Local-coordinate Concept Factorization for Image Clustering

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