Multi-view clustering for multiple manifold learning via concept factorization

Khan, Ghufran Ahmad; Hu, Jie; Li, Tianrui; Diallo, Bassoma; Wang, Hongjun

doi:10.1016/j.dsp.2023.104118

Cited by 9 publications

(1 citation statement)

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“…The article then proposes a new framework, DECCA, which learns embedded representations of documents by maximizing the clustering loss and the reconstruction loss and uses local contrast preservation to improve the accuracy and efficiency of clustering. And Khan et al [28] Wang presents an innovative approach to multiview clustering, tackling the issue that current methods overlook the flow structure of consensus representations in kernel space. This oversight often results in the neglect of the interrelations among different multiviews.…”

Section: Combine the Attention Mechanismmentioning

confidence: 99%

MAGRU: Multi-layer Attention with GRU for Logistics Warehousing Demand Prediction

2024

KSII TIIS

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Warehousing demand prediction is an essential part of the supply chain, providing a fundamental basis for product manufacturing, replenishment, warehouse planning, etc. Existing forecasting methods cannot produce accurate forecasts since warehouse demand is affected by external factors such as holidays and seasons. Some aspects, such as consumer psychology and producer reputation, are challenging to quantify. The data can fluctuate widely or do not show obvious trend cycles. We introduce a new model for warehouse demand prediction called MAGRU, which stands for Multi-layer Attention with GRU. In the model, firstly, we perform the embedding operation on the input sequence to quantify the external influences; after that, we implement an encoder using GRU and the attention mechanism. The hidden state of GRU captures essential time series. In the decoder, we use attention again to select the key hidden states among all-time slices as the data to be fed into the GRU network. Experimental results show that this model has higher accuracy than RNN, LSTM, GRU, Prophet, XGboost, and DARNN. Using mean absolute error (MAE) and symmetric mean absolute percentage error(SMAPE) to evaluate the experimental results, MAGRU's MAE, RMSE, and SMAPE decreased by 7.65%, 10.03%, and 8.87% over GRU-LSTM, the current best model for solving this type of problem.

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Section: Combine the Attention Mechanismmentioning

confidence: 99%