Curse of Dimensionality

Hao, Ning

doi:10.1002/9781118445112.stat00408.pub2

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Another advantage of SAC is its robustness in handling multi-dimensional tasks. High-dimensional tasks are generally challenging to deal with for DRL model due to a phenomenon known as the curse of dimensionality [44]. However, due to the high sample efficiency boosted by entropy maximization, SAC has demonstrated to perform very well in high-dimensional tasks with up to 21 action dimensions [24].…”

Section: A a Primer On Sacmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

An,

Segarra,

Dick

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multiuser beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.

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Section: A a Primer On Sacmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

An,

Segarra,

Dick

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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“…For example, the feature category of a ticket has more than 3,000 options, and the features component and monitor ID of alerts have more than 2,000 and 10,000 options, respectively. Using traditional one-hot encoding [28] methods to process these features would lead to a high-dimensional feature space, resulting in the curse of dimensionality [29]. Additionally, linking alerts to tickets requires the consideration of various combinations of features between them.…”

Section: A Motivating Examplementioning

confidence: 99%

Incident-aware Duplicate Ticket Aggregation for Cloud Systems

Liu¹,

He²,

Chen³

et al. 2023

Preprint

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In cloud systems, incidents are potential threats to customer satisfaction and business revenue. When customers are affected by incidents, they often request customer support service (CSS) from the cloud provider by submitting a support ticket. Many tickets could be duplicate as they are reported in a distributed and uncoordinated manner. Thus, aggregating such duplicate tickets is essential for efficient ticket management. Previous studies mainly rely on tickets' textual similarity to detect duplication; however, duplicate tickets in a cloud system could carry semantically different descriptions due to the complex service dependency of the cloud system. To tackle this problem, we propose iPACK, an incident-aware method for aggregating duplicate tickets by fusing the failure information between the customer side (i.e., tickets) and the cloud side (i.e., incidents). We extensively evaluate iPACK on three datasets collected from the production environment of a large-scale cloud platform, Azure. The experimental results show that iPACK can precisely and comprehensively aggregate duplicate tickets, achieving an F1 score of 0.871∼0.935 and outperforming state-of-the-art methods by 12.4%∼31.2%.

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Identification of Plasmodium Secreted Proteins Based on MonoDiKGap and Distance-Based Top-n-Gram Methods

Liao

Peng

2022

CBIO

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Background: Many malaria infections are caused by Plasmodium falciparum. Accurate classification of the proteins secreted by the malaria parasite, which are essential for the development of anti-malarial drugs, is essential. Objective: To accurately classify the proteins secreted by the malaria parasite. Methods: Therefore, in order to improve the accuracy of the prediction of plasmodium secreted proteins, we established a classification model MGAP-SGD. MonodikGap features (k=7) of the secreted proteins were extracted, and then the optimal features were selected by the AdaBoost method. Finally, based on the optimal set of secreted proteins, the model was used to predict the secreted proteins using the stochastic gradient descent (SGD) algorithm. Results: Our model uses a 10-fold cross-validation set and independent test set in the stochastic gradient descent (SGD) classifier to validate the model, and the accuracy rates are 98.5859% and 97.973%, respectively. Conclusion: This also fully proves that the effectiveness and robustness of the prediction results of the MGAP-SGD model can meet the prediction needs of the secreted proteins of plasmodium.

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Curse of Dimensionality

Abstract: The term “curse of dimensionality” has been widely used in research articles to refer to the technical difficulties caused by increasing dimensions. This short note gives a brief introduction to this term in various subfields of statistics.

Cited by 3 publications

References 11 publications

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

Incident-aware Duplicate Ticket Aggregation for Cloud Systems

Identification of Plasmodium Secreted Proteins Based on MonoDiKGap and Distance-Based Top-n-Gram Methods

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