A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering

Cheng, Zhun; Lu, Zhixiong

doi:10.1155/2018/2879640

Cited by 25 publications

(14 citation statements)

References 28 publications

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“…Data generated in a huge volume in different fields, and it is on continuous growth in size, complexity, and dimensionality [2,3]. A dataset with high dimensionality features its numerous features, but few samples have a direct relation with data mining and machine learning tasks [4,5]. Therefore, these issues of data become a big challenge for extracting potentially useful, and ultimately understandable patterns or information in almost every data mining task.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

View full text Add to dashboard Cite

show abstract

“…By using these features, the partitioning data clustering can be performed to eliminate data redundancy as well as improving the learning accuracy, reducing learning time, and simplifying learning results [43]. In machine learning, the feature selection procedure is widely utilized for reducing dimensions of data, especially when dealing with high dimensional space of data [44]. Some of the feature selection-based data clustering techniques are reviewed below, which are mostly related to our proposed technique in this paper.…”

Section: Data Clustering Techniquesmentioning

confidence: 99%

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Alam

Aziz

Latif

et al. 2020

Sensors

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A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

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“…Statisticians focus on how to construct the best low discrepancy (quasi-random) point sets [27]- [30]. At the same time, variance reduction techniques [31], [32] are widely studied from another side for improving the efficiency of such sampling methods. In our work, we combine the flexibility of variance reduction techniques with the characteristic of effectiveness and fast convergence of low discrepancy sequences.…”

Section: ) Error Bound Of Qmcmentioning

confidence: 99%

Exploring Uplink Achievable Rate for HPO MIMO Through Quasi-Monte Carlo and Variance Reduction Techniques

Gong

Zhang

et al. 2020

IEEE Access

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The power consumption at the receiver side will be dramatically increased in the millimetrewave and massive multiple-input-multiple-output (MIMO) communication systems due to the wide bandwidth and a large number of antennas adopted. A half phase-only MIMO (HPO MIMO) scheme, in which the base station (BS) acquires π-periodic phase measurements of the complex envelop signals was proposed very recently to overcome the above problem. Due to the non-linear nature of HPO MIMO, the valuation of the achievable rate is very challenging. The purpose of the paper is to provide an efficient method for calculating the achievable rate of the HPO MIMO system. By the mutual information theory, we transform the achievable rate into a sum of two high-dimensional integrations. However, calculating those integrations suffers from the enormous computational burden when using the traditional Monte-Carlo method. In order to increase efficiency, a new method by combining quasi-Monte Carlo with a variance reduction technique is proposed. Besides, we derive the probability density function (PDF) of the HPO MIMO system and analyze the uplink achievable rate of the HPO MIMO scheme. Numerical results show that our proposed method is efficient for calculating the achievable rate of the HPO MIMO system. With the proposed method we confirm that HPO MIMO is a promising technology in future low-power communication scenarios.

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A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering

Cited by 25 publications

References 28 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Exploring Uplink Achievable Rate for HPO MIMO Through Quasi-Monte Carlo and Variance Reduction Techniques

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