Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification

Ge, Shaodi; Luo, Liuhong; Liu, Hongjun

doi:10.1007/s40314-020-1091-2

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…The authors use NMF for dimensionality reduction to simplify the data and the relations in the data. To improve the sparseness of the base matrix in incremental NMF, the authors of [34] present a new method, orthogonal incremental NMF algorithm, which combines the orthogonality constraint with incremental learning. This approach adopts batch update in the process of incremental learning.…”

Section: Related Workmentioning

confidence: 99%

A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

Asadi

Povh

2021

Mathematics

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This article uses the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalize the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain time one matrix factor is fixed and the other is updated by moving along the steepest descent direction computed from the penalized objective function and projecting onto the space of non-negative matrices. Our method is tested on two sets of synthetic data for various values of penalty parameters. The performance is compared to the well-known multiplicative update (MU) method from Ding (2006), and with a modified global convergent variant of the MU algorithm recently proposed by Mirzal (2014). We provide extensive numerical results coupled with appropriate visualizations, which demonstrate that our method is very competitive and usually outperforms the other two methods.

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Section: Related Workmentioning

confidence: 99%

A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

Asadi

Povh

2021

Mathematics

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“…The incremental data challenge can be solved by incremental learning. Incremental learning means that a learning system can constantly learn new knowledge from new samples and can save most of the previously known knowledge [5]. However, the traditional federated learning method, which does not consider the weight of incremental data, largely depends on the repetition of the training process, and even leads to the severe decline of global ML model accuracy and ML model deviation.…”

Section: Introductionmentioning

confidence: 99%

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

et al. 2022

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With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.

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Attributed Non-negative Matrix Multi-factorization for Data Representation

Wang

Sun

Guo

et al. 2021

Pattern Recognition and Computer Vision

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Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification

Cited by 5 publications

References 20 publications

A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

Attributed Non-negative Matrix Multi-factorization for Data Representation

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