The trace kernel bandwidth criterion for support vector data description

Chaudhuri, Arin; Sadek, Carol; Kakde, Deovrat; Wang, Haoyu; Hu, Wenhao; Jiang, Hansi; Kong, Seung-Hyun; Liao, Yuewei; Peredriy, Sergiy

doi:10.1016/j.patcog.2020.107662

Cited by 9 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…55–58 It motivated the further investigation of higher-order derivatives of the Boltzmann microcanonical entropy with an additional conserved quantity 59 and has even been used as justification for pattern recognition criteria in computer science. 60…”

Section: Models and Methodsmentioning

confidence: 99%

“…[55][56][57][58] It motivated the further investigation of higherorder derivatives of the Boltzmann microcanonical entropy with an additional conserved quantity 59 and has even been used as justification for pattern recognition criteria in computer science. 60 It is important to note that the attribute ''microcanonical'' has only historical reasons. The analysis method used here has nothing to do with the microcanonical ensemble.…”

Section: Generalized Microcanonical Inflection-point Analysismentioning

confidence: 99%

See 1 more Smart Citation

Secondary-structure phase formation for semiflexible polymers by bifurcation in hyperphase space

Aierken,

Bachmann

2023

Phys. Chem. Chem. Phys.

View full text Add to dashboard Cite

show abstract

Section: Models and Methodsmentioning

confidence: 99%

Section: Generalized Microcanonical Inflection-point Analysismentioning

confidence: 99%

Secondary-structure phase formation for semiflexible polymers by bifurcation in hyperphase space

Aierken,

Bachmann

2023

Phys. Chem. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…Moreover, multi-class SVDD introduces different kernel functions to map samples from the input space to the high-dimensional feature space. At present, polynomial and Gaussian kernel functions are often used in the multi-class SVDD algorithm [40]. A polynomial kernel function can realize the prediction of the summary by subjectively setting the power number, but it is not suitable for the power number of a large order of magnitude.…”

Section: Introductionmentioning

confidence: 99%

In-Wheel Motor Fault Diagnosis Using Affinity Propagation Minimum-Distance Discriminant Projection and Weibull-Kernel-Function-Based SVDD

Liu

Xue

Ding

et al. 2023

Sensors

View full text Add to dashboard Cite

To effectively ensure the operational safety of an electric vehicle with in-wheel motor drive, a novel diagnosis method is proposed to monitor each in-wheel motor fault, the creativity of which lies in two aspects. One aspect is that affinity propagation (AP) is introduced into a minimum-distance discriminant projection (MDP) algorithm to propose a new dimension reduction algorithm, which is defined as APMDP. APMDP not only gathers the intra-class and inter-class information of high-dimensional data but also obtains information on the spatial structure. Another aspect is that multi-class support vector data description (SVDD) is improved using the Weibull kernel function, and its classification judgment rule is modified into a minimum distance from the intra-class cluster center. Finally, in-wheel motors with typical bearing faults are customized to collect vibration signals under four operating conditions, respectively, to verify the effectiveness of the proposed method. The results show that the APMDP’s performance is better than traditional dimension reduction methods, and the divisibility is improved by at least 8.35% over the LDA, MDP, and LPP. A multi-class SVDD classifier based on the Weibull kernel function has high classification accuracy and strong robustness, and the classification accuracies of the in-wheel motor faults in each condition are over 95%, which is higher than the polynomial and Gaussian kernel function.

show abstract