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Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles
2012
IEEE Geoscience and Remote Sensing Letters
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Morphological profiles have been proposed in recent literature, as aiding tools to achieve better results for classification of remotely sensed data. ...
The results show that the NLPCA permits to obtain better classification accuracies than using linear PCA. ...
CLASSIFICATION RESULTS FOR THE CHRIS DATASET USING SVM CLASSIFICATION ALGORITHM. CLASSIFICATION RESULTS FOR THE CHRIS DATASET USING A NEURAL NETWORK CLASSIFICATION ALGORITHM. ). ...
doi:10.1109/lgrs.2011.2172185
fatcat:dciavd5axrdplcxssnukdv6nmq
Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis
2006
Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006
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In this paper, KPCA is used has a preprocessing step to extract relevant feature for classification and to prevent from the Hughes phenomenon. ...
By mapping the data onto another feature space and using nonlinear function, Kernel PCA (KPCA) can extract higher order statistics. ...
The authors would like to thank the IAPR -TC7 for provinding the data and Prof. Paolo Gamba and Prof. Fabio Dell'Acqua of the University of Pavia, Italy, for providing reference data. ...
doi:10.1109/norsig.2006.275232
fatcat:yke3vgpfwfcetpxtofcj6rdbfy
Principal manifolds and Bayesian subspaces for visual recognition
1999
Proceedings of the Seventh IEEE International Conference on Computer Vision
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We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. ...
Abstract We i n vestigate the use of linear and nonlinear principal manifolds for learning lowdimensional representations for visual recognition. ...
Nonlinear Principal Manifolds One of the simplest methods for computing nonlinear principal manifolds is the nonlinear PCA (NLPCA) autoassociative multi-layer neural network [16, 8] shown in Figure ...
doi:10.1109/iccv.1999.790407
dblp:conf/iccv/Moghaddam99
fatcat:uew4mkxlvbeh7bdtjwu4fxg2b4
The new method of Extraction and Analysis of Non-linear Features for face recognition
2012
International Journal of Electrical and Computer Engineering (IJECE)
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In this paper, we introduce the new method of Extraction and Analysis of Non-linear Features (EANF) for face recognition based on extraction and analysis of nonlinear features i.e. ...
In our proposed algorithm, EANF removes disadvantages such as the length of search space, different sizes and qualities of imagees due to various conditions of imaging time that has led to problems in ...
point of h-NLPCA network with the simple solution of PCA. ...
doi:10.11591/ijece.v2i6.1773
fatcat:i32xmprbzvejjj2xoasswuufdi
Nonlinear data description with Principal Polynomial Analysis
2012
2012 IEEE International Workshop on Machine Learning for Signal Processing
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Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Per formance of PCA is however hampered when data exhibits nonlinear feature relations. ...
Successful performance of the proposed PPA is illustrated in dimension ality reduction, in compact representation of non-Gaussian image textures, and multispectral image classification. ...
For those data manifolds lacking the re quired symmetry, nonlinear modifications of PCA would be more appropriate: the residual nonlinear dependence after PCA should be removed. ...
doi:10.1109/mlsp.2012.6349786
dblp:conf/mlsp/LaparraTJCM12
fatcat:yqdrznghqjf4hhcq2bu37nok6u
Markerless gating for lung cancer radiotherapy based on machine learning techniques
2009
Physics in Medicine and Biology
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ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. ...
(artificial neural networks-ANN and SVM). ...
For classification, in addition to SVM, we combine the dimensionality reduction techniques with a three-layer artificial neural network (ANN) for gated lung cancer radiotherapy. ...
doi:10.1088/0031-9155/54/6/010
pmid:19229098
fatcat:ix7y3tmvwfaadjnxplr7tzaeve
Predicting and grouping digitized paintings by style using unsupervised feature learning
2018
Journal of Cultural Heritage
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Classification accuracy and F-score were similar/higher compared to other classification methods using more complex feature learning models (e.g., convolutional neural networks, a supervised algorithm) ...
For feature-based classification of paintings, the macro-averaged F-score was 0.469. ...
PCA is not a component of our nonlinear feature extraction approach, but we performed PCA for comparison. We performed PCA on the image dataset by using singular value decomposition. ...
doi:10.1016/j.culher.2017.11.008
pmid:30034259
pmcid:PMC6051702
fatcat:m6g5bkxjsvel7mbnryvsfgf3ky
Classification of reduction invariants with improved backpropagation
2002
International Journal of Mathematics and Mathematical Sciences
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This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information ...
Higher order centralised scale- invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification ...
and image classification since 1960s. ...
doi:10.1155/s0161171202006117
fatcat:heivlyl43vbpnnrau5upuwaa3u
A Nonlinear Principal Component Analysis of Image Data
2005
IEICE transactions on information and systems
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In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components. ...
In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images. ...
and "Waseda University grant for special research projects, No. 2004B-882." ...
doi:10.1093/ietisy/e88-d.10.2242
fatcat:4ah5m5qnkvgnthuywnlta77tt4
An application of KPCA and SVM in the human face recognition
2013
International Journal of Security and Its Applications
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Face Recognition is more and more researchers' attention, especially the principal Component Analysis method (Principle Component Analysis, PCA) after the application of Face Recognition, Face Recognition ...
Predictably, the two aspects research results in the human visual and non rigid body of will be helpful to find the end solution for face feature extraction and description. ...
However, only the second-order statistics of the image is considered in the PCA method, which cannot be extended for the data of the high order statistics when the nonlinear correlation between pixels ...
doi:10.14257/ijsia.2013.7.6.30
fatcat:dlnrqctl5bcibk6hdyh2r2eehi
CMVF
2003
Proceedings of the 2003 ACM SIGMOD international conference on on Management of data - SIGMOD '03
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for nonlinear correlations can be handled by NLDR. ...
The linear PCA appears at the bottom, the nonlinear neural network is at the top, and the representation of lower dimensional vectors appears in the hidden layer. ...
doi:10.1145/872757.872842
dblp:conf/sigmod/ShenNSHS03
fatcat:vba4n5y4mbgbrnuju5tvjewzma
CMVF
2003
Proceedings of the 2003 ACM SIGMOD international conference on on Management of data - SIGMOD '03
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for nonlinear correlations can be handled by NLDR. ...
The linear PCA appears at the bottom, the nonlinear neural network is at the top, and the representation of lower dimensional vectors appears in the hidden layer. ...
doi:10.1145/872841.872842
fatcat:mbj6f47iijgvna2zekgug6c2ee
Computational Intelligence-Based Biometric Technologies
2007
IEEE Computational Intelligence Magazine
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CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching. ...
Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction. ...
Moghaddam and Yang carried out a comprehensive evaluation of classification methods for recognizing gender from facial images. The best classification performance was that of SVM [87] . ...
doi:10.1109/mci.2007.353418
fatcat:aynahy3ttbesfl3qm3u25gcawq
Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images
2003
Manipulation and Analysis of Biomolecules, Cells, and Tissues
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We have previously shown that neural network classifiers using sets of numerical features computed from fluorescence microscope images were able to recognize all major subcellular location patterns with ...
Current classifiers are limited by under-determined classification boundaries due to the limited number of available images compared to the number of features. ...
for extracting nonlinear principal components. ...
doi:10.1117/12.477903
fatcat:qovbslehijeajbfnedbrhpdlse
Incremental Nonlinear PCA for Classification
[chapter]
2004
Lecture Notes in Computer Science
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The purpose of this study is to propose a new online and nonlinear PCA(OL-NPCA) method for feature extraction from the incremental data. ...
Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. ...
Proposed Classification System In earlier Section 3 we proposed an incremental nonlinear PCA method for nonlinear feature extraction. ...
doi:10.1007/978-3-540-30116-5_28
fatcat:uhxxflm54ngntdr2v66lnnu5dy
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