A review of sparsity-based clustering methods

Oktar, Yigit; Türkan, Mehmet

doi:10.1016/j.sigpro.2018.02.010

Cited by 34 publications

(18 citation statements)

References 82 publications

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“…For the moment, to the best of available knowledge, just a small number of studies relate this problem in a sparse and redundant representations perspective. Some related research samples can be given but no more than in [284,285]. By nature, the sparsity property strongly holds in most real world examples.…”

Section: 4mentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

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253

152

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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.

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Section: 4mentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

Self Cite

253

152

View full text Add to dashboard Cite

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“…Sparse representations have received an increasing attention in many signal and image processing applications. These applications include denoising [21][22][23] , classification [24][25][26] or pattern recognition [27][28][29] . The use of sparse representations for AD is more original and has been considered in less applications such as hyperspectral imaging [30] , detection of abnormal motions in videos [31] , irregular heartbeat detection in electrocardiograms (ECG) or specular reflectance and shadow removal in natural images [15] .…”

Section: Sparse Representations and Dictionary Learningmentioning

confidence: 99%

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Pilastre¹,

Boussouf

d’Escrivan

et al. 2020

Signal Processing

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Spacecraft he.alth monitoring and failure prevention are major issues in space operations. ln recent years, machine le.aming techniques have received an increasing interest in many fields and have been applied to housekeeping telemetiy data via semi-supervised leaming. The idea is to use past telemetry describing normal spacecraft behaviour in order to learn a reference mode(to which can be compared most recent data in order to detect potential anomalies. This paper introduces a new machine learning method for anomaly detection in telemetry time series based on a sparse representation and dictionary Jearning. The main advantage of the proposed method is the possibility to handle multivariate telemetry time series described by mixed continuous and discrete parameters, taking into account the potential correlations be tween these parameters. The proposed method is evaluated on a representative anomaly dataset obtained from real satellite telemetry with an available ground-truth and compared to state-of-the-art algorithrns.

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“…Several algorithms are used for clustering analysis, and they can be roughly divided into four categories [38]: (1) those based on cluster formation methodology, such as top-down, bottom-up, and analytical optimization techniques [39]; (2) those dependent on the cluster model obtained, such as stratification, centroids (e.g., K-means), distribution subspaces, and graph-based models; (3) those obtained via a membership function, which may be further subdivided into hard or soft clustering [40]; and (4) those that use groups to define the distinction between overlapping clusters and are less sensitive to noise because it becomes equally distributed among them [41].…”

Section: Seasonal Clustering Approachmentioning

confidence: 99%

Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM

Liang

et al. 2020

IJGI

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The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.

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A review of sparsity-based clustering methods

Cited by 34 publications

References 82 publications

Electronic Nose and Its Applications: A Survey

Electronic Nose and Its Applications: A Survey

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM

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