Comparison of data reduction techniques based on the performance of SVM-type classifiers

Georgescu, Ramona; Berger, Christian; Willett, Peter; Azam, Md. Shafiul; Ghoshal, Sudipto

doi:10.1109/aero.2010.5446692

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Here, a series of spectra are simultaneously compared through the measure of the covariance (see for example fig 3). This multivariate approach also allows revealing the correlations between different spectral features and consequently is routinely used for data reduction [36]. Indeed PCA provides new data, which are linear combinations of the initial data.…”

Section: Identification Of Samplesmentioning

confidence: 99%

Review in terahertz spectral analysis

Haddad

Bousquet

Canioni

et al. 2013

TrAC Trends in Analytical Chemistry

159

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International audienceNowadays terahertz spectroscopy is a well-established technique and recent progresses in technology demonstrated that this new technique is useful for both fundamental research and industrial applications. Varieties of applications such as imaging, non destructive testing, quality control are about to be transferred to industry supported by permanent improvements from basic research. Since chemometrics is today routinely applied to IR spectroscopy, we discuss in this paper the advantages of using chemometrics in the framework of terahertz spectroscopy. Different analytical procedures are illustrates. We conclude that advanced data processing is the key point to validate routine terahertz spectroscopy as a new reliable analytical technique

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Section: Identification Of Samplesmentioning

confidence: 99%

Review in terahertz spectral analysis

Haddad

Bousquet

Canioni

et al. 2013

TrAC Trends in Analytical Chemistry

159

View full text Add to dashboard Cite

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“…They use filtering algorithm for reduction of transmitted data for increasing the lifetime of the network [2]. Ramona Georgescu, Christian R. Berger, et.al, proposed 4 data reduction techniques such as Principal Component Analysis (PCA), Partial Least Square (PLS), Structurally Random Matrices (SRM), and Orthogonal Matching Pursuit OMP) [6]. Extensive research has been performed on different image and text compression techniques.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Data Reduction Technique using Change Coding for Wireless Visual Sensor Network

Kapas¹

2019

IJRASET

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Wireless Visual Sensor Network is one of the most important applications of Wireless Sensor Network which provide a sensor based intelligent distributed system. Visual Sensor Networks has less complexity, identical performance and better quality of service. Visual Sensor Network contains a large number of camera nodes for providing a visual view of an object. The majority of industries use a network of cameras in order to obtain real-time data for monitoring and control. These networks are wired based in which data is sent over a wired link and power is available from wall power supplies. By using this system, the Captured Image from Camera and dataset is created .this data set is stored in local device which is raspberry pi. Raspberry pi has 1 GB RAM and 1.4 GHz frequency as well as it has unique features than other embedded system devices. This captured Dataset needs to a reduction in the Images for uploading to the server. Before this some morphological image processing techniques are applied for getting good quality of an image. Then for reduction of those images, change coding technique is applied. In change coding XOR operation of two adjacent frames is takes place. Finally, an image will be uploading to the Server.

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“…In PSVM, the separating planes are not bounding planes, but can be thought of as proximal planes, around which the instances of each class are clustered, and which are pushed as far apart as possible [16]. This formulation can also be interpreted as regularized least squares.…”

Section: Proximal Support Vector Machinementioning

confidence: 99%

“…Recently, structurally random matices (SRMs) have been proposed as a fast and highly efficient compressed sensing method that guarantees optimal performance [16].…”

Section: Structurally Random Matricesmentioning

confidence: 99%

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Comparison of data reduction techniques based on SVM classifier and SVR performance

Zhao¹,

Georgescu²,

Willett³

2011

SPIE Proceedings

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In this work, we applied several data compression techniques to simulated data and the Turbofan engine degradation simulation data set from NASA, with the goal of comparing their performance when coupled with the Support Vector Machine (SVM) classifier and the SVM regression (SVR) predictor. We consistently attained correct rates in the neighborhood of 90% for simulated data set, with the Principal Component Analysis (PCA), Sparse Reconstruction by Separable Approximation (SpaRSA) and Partial Least Squares (PLS) having a slight edge over the other data reduction methods for data classification. We achieved 22% error rate with SRM for the Turbofan data set 1 and 40% error rate with PCA for Turbofan data set 2. Throughout the tests we have performed, PCA proved to be the best data reduction method.

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Comparison of data reduction techniques based on the performance of SVM-type classifiers

Cited by 9 publications

References 10 publications

Review in terahertz spectral analysis

Review in terahertz spectral analysis

An Efficient Data Reduction Technique using Change Coding for Wireless Visual Sensor Network

Comparison of data reduction techniques based on SVM classifier and SVR performance

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