Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method

Pang, Yanwei; Zhang, Lei; Liu, Zhengkai; Yu, Nenghai; Li, Houqiang

doi:10.1007/11538059_13

Cited by 74 publications

(19 citation statements)

References 10 publications

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“…The original LPP is an unsupervised method. Two unsupervised methods, neighborhood preserving projection (NPP) [2,3] and neighborhood preserving embedding (NPE) [4], are inspired by LPP. NPP and NPE are similar to LPP in the sense of locality preserving.…”

Section: Some Dimensionality Reduction Methodsmentioning

confidence: 99%

Bilateral filtering inspired locality preserving projections for hyperspectral images

Pan

et al. 2015

Neurocomputing

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Section: Some Dimensionality Reduction Methodsmentioning

confidence: 99%

Bilateral filtering inspired locality preserving projections for hyperspectral images

Pan

et al. 2015

Neurocomputing

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“…Feature extraction is a significant process of obtaining distinctiveness from time series data sets. Aiming at discriminating the reference class from other classes, feature selection can be considered as a data-compression process which removes irrelevant information and preserves relevant information from the raw data [16]. Typically, feature extraction approach applied to raw signals precedes the classification procedure.…”

Section: Preprocessing Based On Linear Correlation Analysismentioning

confidence: 99%

Multisensor Based Neutral Function Identification of Solenoid Valve

Guo

et al. 2014

International Journal of Distributed Sensor Networks

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Condition monitoring of hydraulic systems has been using automatic control in industrial system. In this paper, a sensor network based intelligent control is proposed for efficient solenoid valve identification. The detection system learns to detect the change of output pressure of multipoints that represent a more complicated task. Linear correlation analysis is introduced for feature extraction, which allows for a significant reduction in the dimension of original data without compromising the change detection performance. Implemented as an agent identifying the valve types under measurement, the support vector machine classifier achieves a significant high accuracy in identification and an increase in deployment efficiency. Experimental results prove that the system is feasible for application designs and could be implemented on technological platforms.

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“…But their properties of locality preserving are fairly significative. In order to overcome this drawback, Pang et al proposed a linear dimensionality reduction method, named Neighborhood Preserving Projection (NPP) [7] which aims at preserving the local manifold structure. Although NPP is successful in many domains, it nevertheless suffers from limitations.…”

Section: Introductionmentioning

confidence: 99%

“…NPP is a linear approximation to LLE [3,7]. NPP finds a linear transformation matrix A by minimizing the following objective function…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Neighborhood Preserving Discriminant Projections for Face Recognition

Wang

Shi

Hou

2009

2009 2nd International Congress on Image and Signal Processing

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Feature extraction is an important step for face recognition. The capability of feature extraction directly influences the performance of face recognition. Recently, some manifold learning algorithms have drawn much attention. Among them, neighborhood preserving projections is one of the most promising feature extraction techniques. Though NPP has been applied in many fields, it has limitations to solve recognition task. In this paper, a novel feature extraction method called orthogonal neighborhood preserving discriminant projections (ONPDP) is proposed. The aim of ONPDP is to preserve the within-class neighboring geometric relation by taking into account class label information, while maximizing the betweenclass distance. Two abilities of manifold learning and classification are combined into the properties of the proposed algorithm. In addition, ONPDP orthogonalizes the set of the projection transformation vectors of the face subspace by GramSchmidt orthogonalization and thus the stronger discriminating power can be obtained. Experiment results on FERET and ORL face databases demonstrate the effectiveness of the proposed method.

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Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method

Cited by 74 publications

References 10 publications

Bilateral filtering inspired locality preserving projections for hyperspectral images

Bilateral filtering inspired locality preserving projections for hyperspectral images

Multisensor Based Neutral Function Identification of Solenoid Valve

Orthogonal Neighborhood Preserving Discriminant Projections for Face Recognition

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