Suchergebnisse - randomized matrix-factorization algorithm

Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization von Chen, Yong-Yong, Xu, Fang-Fang

“… Orthogonal nonnegative matrix factorization (ONMF) is widely used in blind image separation problem, document classification, and human face recognition …”
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Novel Distributed Multimedia Recommendation Systems Using Personalized Information von Chang, Shih Yu, Wu, Hsiao-Chun, Yan, Kun, Huang, Scott Chih-Hao, Wu, Yiyan

“… In this paper, we propose a novel distributed multimedia recommendation system (DMRS) to address personalized preference by use of the matrix-sketching …”
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Federated Multimedia Recommendation Systems with Privacy Protection von Chang, Shih Yu, Wu, Hsiao-Chun, Yan, Kun, Huang, Scott Chih-Hao, Wu, Yiyan

“… In this big data era, distributed machine learning and global data pooling have become quite critical in practical implementation of any multimedia …”
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Spectrum-Revealing Randomized Matrix Factorization: Theory and Algorithms von Xiao, Jianwei

“… We also develop an efficient and effective randomized algorithm for computing this factorization …”
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An Algorithm for Randomized Nonnegative Matrix Factorization and Its Global Convergence von Masuda, Takao, Migita, Tsuyoshi, Takahashi, Norikazu

“… Nonnegative Matrix Factorization (NMF) is to decompose a given nonnegative matrix into two nonnegative factor matrices …”
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Randomized Algorithms for Symmetric Nonnegative Matrix Factorization von Hayashi, Koby, Aksoy, Sinan G, Ballard, Grey, Park, Haesun

“… Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-rank matrix and its transpose …”
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Randomized Algorithms for Low-Rank Matrix Factorizations: Sharp Performance Bounds von Witten, Rafi, Candès, Emmanuel

“… The development of randomized algorithms for numerical linear algebra, e.g. for computing approximate QR and SVD factorizations, has recently become an intense area of research …”
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An Efficient Randomized Low-Rank Matrix Factorization with Application to Robust PCA von Kaloorazi, Maboud F., Chen, Jie, Li, Fei, Wu, Dan

“… Low-rank matrix factorization algorithms using the randomized sampling paradigm have recently gained momentum, owing to their computational efficiency, high accuracy, robustness, and efficient parallelization …”
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Fast Parallel Randomized Algorithm for Nonnegative Matrix Factorization with KL Divergence for Large Sparse Datasets von Nguyen, Duy Khuong, Tu, Bao Ho

“… Nonnegative Matrix Factorization (NMF) with Kullback-Leibler Divergence (NMF-KL) is one of the most significant NMF problems and equivalent to Probabilistic Latent Semantic Indexing (PLSI …”
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Randomized nonnegative matrix factorization von Erichson, N. Benjamin, Mendible, Ariana, Wihlborn, Sophie, Kutz, J. Nathan

“… •A novel randomized hierarchical alternating least squares algorithm for NMF.•The randomized algorithm scales up to big data …”
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Sequential randomized matrix factorization von Bopardikar, Shaunak D., Nair, Sujit S., Rai, Rahul

“… This work extends recent research which uses randomized algorithms for performing matrix factorization.We develop and analyze a randomized method for incremental matrix factorization under certain uniform low-rankedness assumption …”
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Towards a more reliable privacy-preserving recommender system von Jiang, Jia-Yun, Li, Cheng-Te, Lin, Shou-De

“… Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF …”
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SketchNE: Embedding Billion-Scale Networks Accurately in One Hour von Xie, Yuyang, Dong, Yuxiao, Qiu, Jiezhong, Yu, Wenjian, Feng, Xu, Tang, Jie

“… We study large-scale network embedding with the goal of generating high-quality embeddings for networks with more than 1 billion vertices and 100 billion …”
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Randomized Truncated Pivoted QLP Factorization for Low-Rank Matrix Recovery von Kaloorazi, Maboud Farzaneh, Chen, Jie

“… In this letter, we first present a rank-revealing matrix factorization algorithm by using randomization called randomized truncated pivoted QLP (RTp-QLP …”
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Robust and fast infrared small target detection based on pareto frontier optimization von Xu, Long, Wei, Ying, Zhang, Haoyun, Shang, Shengxing

“… The matrix factorization-based algorithms can work well in addressing this issue. In this paper, to address the residual over-optimized issue in matrix factorization …”
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Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares von Sapper, Benjamin C., Youn, Sean, Henze, Daven K., Canagaratna, Manjula, Stark, Harald, Jimenez, Jose L.

“… Weighted positive matrix factorization (PMF) has been used by scientists to find small sets of underlying factors in environmental data …”
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Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization von Ni, Yonghui, He, Jianghua, Chalise, Prabhakar

“… In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization …”
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Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization von Haba, Renichiro, Ohzeki, Masayuki, Tanaka, Kazuyuki

“… Among its many applications, nonnegative/binary matrix factorization stands out for its complexity and relevance in unsupervised machine learning …”
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Randomized matrix factorization for Kalman filtering von Bopardikar, Shaunak D.

“… This paper discusses an application of randomized algorithms for matrix factorization to the classic Kalman filtering technique to estimate the state of a linear dynamical system …”
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Implementation of Adaptive Partial Discharge Denoising in Resource-Limited Embedded Systems via Efficient Time-Frequency Matrix Factorization von Yan, Yuan, Trinchero, Riccardo, Stievano, Igor Simone, Li, Hongjie

“… an effective PD denoising algorithm is crucial to improve their robustness to on-site noise. Yet, denoising PD signals in the monitoring devices is challenging primarily due to three critical reasons, i.e …”
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