Sparse Bayesian learningLong short-term memoryModel-driven algorithms on distributed compressive sensing with multiple measurement vectors (MMVs) have been generally based on the assumption that the vectors in the signal matrix are jointly sparse. However, the signal matrix in many practical scenarios ...
Mean field variational Bayes for continuous sparse signal… 热度: Virginia's Foundation Blocks for Early Learning 热度: a comparison of state-of-the-art algorithms for learning bayesian network structure from continuous data 热度: 相关推荐 Distributed Bayes Blocks for Variational Bayesian Learning...
Such a compound vector is sparse (as most compounds do not typically contain more than 5 or 6 atom types). Each component of the vector contains the unit normalized amount of the atom in the formula. For example, for H2O, the component corresponding to H would have a value of 0.66 ...
Bi J, Zhang C (2018) An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme. Knowl-Based Syst 158:81–93 ArticleGoogle Scholar Bian J, Xiong H, Cheng W, Hu W, Guo Z, Fu Y (2017) Multi-party sparse discrimina...
• Communication-Mitigated Federated Learning (CMFL) [60]. • Bayesian compression [61] Advantage: • Reduces the required communication bandwidth and improves the scalability by sending only the important gradients (sparse update). • Enhances energy-efficiency and throughput. • Slashing the...
Dai, J., So, H.C.: Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation. IEEE Trans. Signal Process. 66(3), 744–756 (2017) Article MathSciNet Google Scholar Doucet, A., Gordon, N.J., Krishnamurthy, V.: Particle filters for state estimation of jump...
The sparsity in DCCS’s data indicates the unavailability of specific data, missing any details or the data contains only zeros. It can also be defined as the data in which only a small fraction of it is useful; extracting useful information out of sparse data is a challenging issue. It ...
This method is compared with other methods, i.e., block orthogonal matching pursuit (BOMP), block CoSaMp, block smoothed ℓ norm based method (BSL0), spectral projected gradient (SPG L1), and block sparse Bayesian learning (BSBL). The signal recovery performance of ...
Article Google Scholar Kadam, V.J., Jadhav, S.M., Vijayakumar, K.: Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. J. Med. Syst. 43(8), 1–11 (2019) Article Google Scholar Download references ...
[234] use LSTM for load forecasts in their approach to providing harmonic state estimation using regression analysis for power flow calculations and sparse Bayesian learning. Finally, in [235], the authors consider decomposing the demand of LV substations into traditional load, flexible load and ...