Large-Scale Regression: A Partition Analysis of the Least Squares Multisplitting

Inghelbrecht, Gilles; Pintelon, Rik; Barbé, Kurt

doi:10.1109/tim.2019.2925880

“…Since the dependent variable to be regressed is the continuous variable data, the Ordinary Least Squares (OLS) model [22] is suitable to evaluate the multiple regression model, whose representation is as follows:…”

Section: Traffic Data Preprocessing Modelmentioning

confidence: 99%

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Guo,

Zhu,

Wang

2024

Applied Sciences

0

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In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model.

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“…Input: Unwrapped phaseŶ ∈ R F ×M ×N , Output: Phase feature, P for m = 1 to M do for f = 1 to F do Linear regression for each f and m: β f,m = (Ξ T Ξ) −1 Ξ Tŷ f,m . Define η f,m ∈ R N as the deviation betweenŷ f,m and the regression line: obtained using least squares method [14], ofŷ f,m .…”

Section: Algorithm 2 Phased-based Feature Extractionmentioning

confidence: 99%

Moving Object Classification with a Sub-6 GHz Massive MIMO Array Using Real Data

Manoj

¹

,

Tian

²

,

Gunnarsson

³

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2

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Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-ofsight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

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“…We denote Ŷ as the unwrapped phase of Y and ŷf,m ∈ R N as the vector representing the phase snapshot obtained from Ŷ for the f -th subcarrier and m-th RF chain. We define ξ = [1, 2, ..., N ] T ∈ R N as the indexes of the snapshots, 1 N ∈ R N as the column unit vector, and obtained using least squares method [14], of ŷf,m .…”

Section: Algorithm 1 Amplitude-based Feature Extractionmentioning

confidence: 99%

Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data

Manoj

¹

,

Tian

²

,

Gunnarsson

³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-ofsight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

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Large-Scale Regression: A Partition Analysis of the Least Squares Multisplitting

Cited by 7 publications

References 25 publications

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Moving Object Classification with a Sub-6 GHz Massive MIMO Array Using Real Data

Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data

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