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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods.
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PMID:A unified framework for subspace face recognition. 1574 96

We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantage of this distance measure is that it can be embedded in most image classification techniques such as SVM, LDA, and PCA. The embedding is rather efficient by involving a transformation referred to as Standardizing Transform (ST). We show that ST is a transform domain smoothing. Using the Face Recognition Technology (FERET) database and two state-of-the-art face identification algorithms, we demonstrate a consistent performance improvement of the algorithms embedded with the new metric over their original versions.
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PMID:On the Euclidean distance of images. 1611 71

Linear subspace methods that provide sufficient reconstruction of the data, such as PCA, offer an efficient way of dealing with missing pixels, outliers, and occlusions that often appear in the visual data. Discriminative methods, such as LDA, which, on the other hand, are better suited for classification tasks, are highly sensitive to corrupted data. We present a theoretical framework for achieving the best of both types of methods: An approach that combines the discrimination power of discriminative methods with the reconstruction property of reconstructive methods which enables one to work on subsets of pixels in images to efficiently detect and reject the outliers. The proposed approach is therefore capable of robust classification with a high-breakdown point. We also show that subspace methods, such as CCA, which are used for solving regression tasks, can be treated in a similar manner. The theoretical results are demonstrated on several computer vision tasks showing that the proposed approach significantly outperforms the standard discriminative methods in the case of missing pixels and images containing occlusions and outliers.
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PMID:Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling. 1652 21

We describe the optimal high-level postprocessing of single-voxel (1)H magnetic resonance spectra and assess the benefits and limitations of automated methods as diagnostic aids in the detection of recurrent brain tumor. In a previous clinical study, 90 long-echo-time single-voxel spectra were obtained from 52 patients and classified during follow-up (30/28/32 normal/non-progressive tumor/tumor). Based on these data, a large number of evaluation strategies, including both standard resonance line quantification and algorithms from pattern recognition and machine learning, were compared in a quantitative evaluation. Results from linear and non-linear feature extraction, including ICA, PCA and wavelet transformations, and also the data from resonance line quantification were combined systematically with different classifiers such as LDA, chemometric methods (PLS, PCR), support vector machines and ensemble methods. Classification accuracy was assessed using a leave-one-out cross-validation scheme and the area under the curve (AUC) of the receiver operator characteristic (ROC). A regularized linear regression on spectra with binned channels reached 91% classification accuracy compared with 83% from quantification. Interpreting the loadings of these regressions, we find that lipid and lactate signals are too unreliable to be used in a simple machine rule. Choline and NAA are the main source of relevant information. Overall, we find that fully automated pattern recognition algorithms perform as well as, or slightly better than, a manually controlled and optimized resonance line quantification.
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PMID:Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors. 1664 60

A kernel based generalized discriminant analysis (GDA) technique is proposed for the classification of stars, galaxies, and quasars. GDA combines the LDA algorithm with kernel trick, and samples are projected by nonlinear mapping onto the feature space F with high dimensions, and then LDA is conducted in F. Also, it could be inferred that GDA which combines the extension of Fisher's criterion with kernel trick is complementary to kernel Fisher discriminant framework. LDA, GDA, PCA and KPCA were experimentally compared with these three different kinds of spectra. Among these four techniques, GDA obtains the best result, followed by LDA, and PCA is the worst. Although KPCA is also a kernel based technique, its performance is not satisfactory if the selected number of the principal components is small, and in some cases, it appears even worse than LDA, a non-kernel based technique.
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PMID:[Spectra classification based on generalized discriminant analysis]. 1720 63

This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, Locality Preserving Projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications.
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PMID:Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. 1856 3

In this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L(2) distance, the gradient distance is able to reveal latent manifold structure of face patterns. By using TPNMF decomposition, the high-dimensional face space is transformed into a local topology preserving subspace for face recognition. In comparison with PCA, LDA, and original NMF, which search only the Euclidean structure of face space, the proposed TPNMF finds an embedding that preserves local topology information, such as edges and texture. Theoretical analysis and derivation given also validate the property of TPNMF. Experimental results on three different databases, containing more than 12,000 face images under varying in lighting, facial expression, and pose, show that the proposed TPNMF approach provides a better representation of face patterns and achieves higher recognition rates than NMF.
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PMID:Topology preserving non-negative matrix factorization for face recognition. 1839 Mar 65

We present a novel family of data-driven linear transformations, aimed at finding low-dimensional embeddings of multivariate data, in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods such as to enhance their performance. Furthermore, our technique is the only one, to the best of our knowledge, that reflects in the resulting embedding both the data coordinates and pairwise relationships between the data elements. Even more so, when information on the clustering (labeling) decomposition of the data is known, this information can also be integrated in the linear transformation, resulting in embeddings that clearly show the separation between the clusters, as well as their internal structure. All of this makes our technique very flexible and powerful, and lets us cope with kinds of data that other techniques fail to describe properly.
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PMID:Robust linear dimensionality reduction. 1857 73

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.
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PMID:Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis. 1907 79

Raman spectroscopy has the ability to provide differential diagnosis of different cancers with high sensitivity and specificity. A major limitation in its clinical application is the weak nature of Raman signal, which inhibits scanning large surface areas of tissues. In bladder cancer diagnosis, fluorescence-guided endoscopy with 5-aminolevulinic acid (5-ALA) has gained interest as a technique that can provide such spatial differentiation, thus improving early detection and more complete removal of superficial tumors. However, several studies have demonstrated the poor specificity of this modality. Combining fluorescence with Raman spectroscopy could improve its diagnostic capability. However, little is known about the effect of agents such as 5-ALA on Raman spectra of tissue. In this paper, we present measuring Raman spectroscopy from benign and malignant bladder tissues in the presence of 5-ALA and attempt to evaluate the potential to discriminate between different pathologies. Raman spectra were recorded from 92 bladder biopsies without 5-ALA and 38 biopsies with 5-ALA using a Raman microspectrometer system at 830nm excitation. Empirical and multivariate statistical techniques were used for data analysis. Algorithms were developed to determine the effect of 5-ALA on tissue and its influence on the prediction ability of a preliminary benign/malignant prediction model. In samples with 5-ALA, an overall decrease in Raman intensity was observed when compared to the Raman spectra from samples without 5-ALA. Additionally, differences in relative intensities at 1270 and 1330cm(-1) were also noted. However, significant differences were observed in the Raman spectra of benign and malignant samples with 5-ALA indicating the potential of using Raman spectroscopy for discriminating bladder cancer in the presence of 5-ALA. The Principal-Component fed Linear-Discriminant Analysis (PCA/LDA) algorithm derived from biopsies in the absence of 5-ALA used to predict biopsies in the presence of 5-ALA resulted in an overall sensitivity and specificity of 42.6% and 71.1%, respectively. This suggests the presence of 5-ALA in tissue affects the Raman spectra. A PCA/LDA algorithm based on fluorescence information (i.e. PpIX fluorescence positive or negative) and the Raman spectrum of 5-ALA biopsies, had a sensitivity and specificity of 100% and 80.8%, respectively. This study demonstrates that applying 5-ALA affects the Raman spectra of bladder tissues. However, benign/malignant differentiation can be accomplished with a preliminary PCA/LDA algorithm, suggesting the potential of a combined diagnostic modality in vivo.
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PMID:Raman spectroscopy of bladder tissue in the presence of 5-aminolevulinic acid. 1936 51


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