Modified Orthogonal Neighborhood Preserving Projection for Face Recognition

Koringa, Purvi A.; Shikkenawis, Gitam; Mitra, Suman K.; Parulkar, S. K.

doi:10.1007/978-3-319-19941-2_22

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In this paper, we have incorporated various weights [16] to the stress terms. We have introduced a new weight function, the Zshaped function, based on intraclass information and the structure of the data set discussed in [14,15]. The Z-shaped function is used to assign weights to the nearest neighbours of the points.…”

Section: Discussion and Findingsmentioning

confidence: 99%

Section: Z-shaped Weight Functionmentioning

confidence: 99%

“…Z-shaped weight function is dependent on intraclass information of the dataset discussed in [14,15]. In [14,15], the Zshaped function is used to assign maximum weights to nearest neighbours. As the distance increases, the weights will be adjusted through a piecewise function give in Eq.…”

Section: Z-shaped Weight Functionmentioning

confidence: 99%

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Supervised Regularized Multidimensional Scaling Using Weighted Stress Measure

Islam¹,

Bhuiyan²,

Jahan³

2022

ujam

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Supervised Regularized Multidimensional Scaling (SRMDS), a non-linear variant of classical Multi-Dimensional Scaling (cMDS) is proposed recently which involves Radial basis function. The method is focused on the effective selection of centers of the radial basis functions in transforming data from a higher dimensional space to a lower dimension. The transformation matrix is determined by minimizing stress. Weights of components of the stress that are of great importance for classification of data got less focus in Supervised Regularized Multidimensional Scaling. In this article, we have investigated several forms of non-linear functions which may be used as weights of the stress measure. A new form of Z-shape weight function dependent on intraclass information of the dataset is introduced which prefers to preserve global structure of the dataset. The efficiency of the proposed approach is illustrated on several benchmarking datasets which shows that the weighted Supervised Regularized Multidimensional Scaling (WSRMDS) outperforms the base method and some other state of the art approaches such as Linear Discriminant Analysis (LDA), and Supervised Principal Component Analysis (SPCA). Observing the finding of this research, among different weight functions, Z-shape weight function is recommended to use since it works better then any other weight functions for most of the data sets.

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Section: Discussion and Findingsmentioning

confidence: 99%