On some variants of locality preserving projection

Shikkenawis, Gitam; Mitra, Suman K.

doi:10.1016/j.neucom.2015.01.100

Cited by 35 publications

(18 citation statements)

References 23 publications

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“…It is to be noted that the graphs shown here are not continuous, for better visual comparison, results of subsequent dimensions are joined by a line. Performance of proposed K- ESLPP-MD [13] is on par with that of K-LPDP. However, it can be observed that, for both the databases, the proposed approach achieves more than 96% face recognition accuracy, out performing all the competing approaches.…”

Section: Methodsmentioning

confidence: 94%

“…Proposed Kernelized version of LPDP is first compared with conventional LPDP approach to show the advantage of using the kernel mapping. K-LPDP is also compared with some kernelized variants of some locality preserving dimensionality reduction approaches such as K-LPP [6], Extended LPP (K-ELPP) [11], Supervised LPP (K-SLPP) [17] and Extended Supervised LPP with Modified Distance (K-ESLPP-MD) [13]. Out of different kernel functions used for the experimentation purpose, Gaussian kernel produced the best results, hence all the experiments of the kernel based variants reported in this article are carried out with the Gaussian kernel.…”

Section: Methodsmentioning

confidence: 99%

“…Here, the discrimination between classes was achieved by considering the mean vectors of different classes. Extended Supervised LPP with Modified Distance (ESLPP-MD) [13] modifies the distances based on the class label information to make data points from same class as strong neighbors and data points from different classes as weak neighbors. Hence, explicit manipulation of distances is required.…”

Section: Introductionmentioning

confidence: 99%

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Kernelization of locality preserving discriminant projection

Shikkenawis

Mitra

2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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In recent years, non-linear dimensionality reduction approaches have become popular due to their suitability to capture the non-linearities present in the data. However, these are not applicable for recognition related applications as representation of new data points is not defined in the reduced subspace. Hence, to explore the non-linearities of the data, local structure preserving approaches received considerable attention. These approaches keep the local information of the data points intact in the lower dimensional space as well. Locality Preserving Discriminant Projection (LPDP) not only preserves the neighborhood information for each data point, but also tries to discriminate data points from different classes using their class labels. The performance of such neighborhood information preserving dimensionality reduction techniques do not guarantee to capture complex non-linearities present in the data. To address this issue, kernel functions that map the data in a nonlinear feature space before applying the dimensionality reduction approach are widely used. This article discusses Kernelization of LPDP which explores complex non-linear changes of face images due to facial expression, illumination, pose and environmental changes. The proposed approach is applied for face recognition on some of the benchmark databases with such variability.

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Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kernelization of locality preserving discriminant projection

Shikkenawis

Mitra

2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

Self Cite

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“…By solving the generalized eigenvalue problem, the transform matrix A can be computed via Eqn. (11).…”

Section: Function: Sdae_trainmentioning

confidence: 99%

“…Motivated by Refs. [11,12], the individual-specific properties in EEG are expected to be better extracted and fused to reflect the MW levels. Finally, a new MW classification framework, an ensemble SDAE classifier with local information preservation (denoted by EL-SDAE), is proposed and validated in this work.…”

Section: Introductionmentioning

confidence: 99%

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subjectspecific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

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Supervised sparse preserving projections model based on distributed principal component analysis for chemical process monitoring

Ding

2022

Can J Chem Eng

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For the monitoring of large‐scale chemical processes, the distributed method is often used to extract local feature information and model the extracted local feature information to obtain a process monitoring model. But the distributed process monitoring model often contains more process variables, which makes the local information of the process data flooded. To make up for the insufficient extraction of local information in traditional distributed process monitoring, supervised sparse preserving projections model based on distributed principal component analysis (DPCA‐SSPP) is proposed in this paper. First, the process data are decomposed by the PCA algorithm, and the principal component space and residual space are obtained. Second, the variables of each sub‐block are selected according to the maximum correlation criterion, and the SSPP process monitoring model is established for each sub‐block. Finally, the monitoring results of each sub‐block are combined together to form a global monitoring result through the Bayesian information fusion strategy. The proposed scheme can be proved to be effective through the simulation on a nonlinear numerical example and the Tennessee Eastman benchmark (TE) process.

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On some variants of locality preserving projection

Cited by 35 publications

References 23 publications

Kernelization of locality preserving discriminant projection

Kernelization of locality preserving discriminant projection

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Supervised sparse preserving projections model based on distributed principal component analysis for chemical process monitoring

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