Comparing Four Dimension Reduction Algorithms to Classify Algae Concentration Levels in Water Samples Using Hyperspectral Imaging

Face recognition technology has evolved as an enchanting solution to address the contemporary needs in order to perform identification and verification of identity claims. By advancing the feature extraction methods and dimensionality reduction techniques in the application of pattern recognition, a number of face recognition systems has been developed with distinct degrees of success. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP preserve the local structure of face image space which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). This paper focuses on a systematic analysis of locality-preserving projections and the application of LPP in combination with an existing technique This combined approach of LPP through MPCA can preserve the global and the local structure of the face image which is proved very effective. Proposed approach is tested using the AT & T face database. Experimental results show the significant improvements in the face recognition performance in comparison with some previous methods.

Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.

Remote sensing Hyperspectral Image (HSI) comprises significant information about the earth’s surface which is actually acquired by hundred of narrow and adjacent spectral bands. The intended performance of classification accuracy does not attain due to the volume of the original HSI dataset and the enormous quantity of spectral bands. As such, dimensionality reduction approaches using feature extraction and selection are typically adopted to enhance classification performance. The unsupervised Principal Component Analysis (PCA), as well as the supervised Linear Discriminant Analysis (LDA), are commonly used as linear feature extraction methods for feature subspace detection. However, due to considering the effects of global variation, both PCA and LDA fail to extract local characteristics of HSI. In this paper, we propose a segmented LDA-based (SLDA) feature extraction where we apply the LDA in a segmented way to extract better local characteristics as well as global characteristics from the HSI. Per-pixel classification using a Support Vector Machine (SVM) is applied to our proposed SLDA method, PCA, Segmented-PCA (SPCA), and LDA on the Indian Pines agricultural HSI dataset. The experimental results show that the overall classification performance of SLDA (90.60%) remarkably outperforms all the other investigated methods: PCA (85.55%), SPCA (86.96%), LDA (86.45%), and the complete original dataset without employing any feature reduction method (83.10%). The proposed SLDA also requires the least amount of space complexity in different implementation scenarios.

Complete neighborhood preserving embedding for face recognition

Dimensionality reduction plays a significant role for the performance of large-scale image retrieval. In this paper, various dimensionality reduction methods are compared to validate their own performance in image retrieval. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and HSV (Hue, Saturation, Value) histogram are extracted as image features. Second, the Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA), Local Fisher Discriminant Analysis (LFDA), Isometric Mapping (ISOMAP), Locally Linear Embedding (LLE), and Locality Preserving Projections (LPP) are respectively applied to reduce the dimensions of SIFT feature descriptors and color information, which can be used to generate vocabulary trees. Finally, through setting the match weights of vocabulary trees, large-scale image retrieval scheme is implemented. By comparing multiple sets of experimental data from several platforms, it can be concluded that dimensionality reduction method of LLE and LPP can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

In the present work, appearance-based face recognition method called the Laplacianface approach is used. The face images are mapped into a face subspace for analysis by using Locality Preserving Projections(LPP). The technique is different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space. The main goal of LPP is to preserve neighbourhood structure of the data set optimally and hence obtain a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the Eigen functions of the Laplace Beltrami operator on the face manifold. Hence, by using this approach undesired variations because of facial expression, changes in lighting conditions, and pose may be eliminated or reduced. Performance analysis of face recognition is carried out on standard databases using both LPP technique and PCA technique. For comparison purpose, PCA with ANN classifier based on Back Propagation Feed Forward Neural Network is also developed and being used for training the input face images and then testing. LPP approach outperforms PCA with ANN, and provides better face representation and also achieves lower error rate.

Learning representations that disentangle hidden explanatory factors in data has proven beneficial for effective pattern classification. Slow feature analysis (SFA) is a nonlinear dimensionality reduction technique that provides a useful representation for classification if the training data is sequential and transitions between classes are rare. The pattern discrimination ability of SFA has been attributed to the equivalence of linear SFA and linear discriminant analysis (LDA) under certain conditions. LDA, however, is often outperformed by locality preserving projections (LPP) when the data lies on or near a low-dimensional manifold. Here, we take a unified manifold learning perspective on LPP, LDA and SFA. We suggest that the discrimination ability of SFA is better explained by its relation to LPP than to LDA, and give an example of a situation where linear SFA outperforms LDA. We then propose a novel supervised manifold learning architecture that combines hierarchical nonlinear expansions, as commonly used for SFA, with supervised LPP. It learns a nonlinear parametric data representation that explicitly takes both the class labels and the manifold structure of the data into account. As an experimental validation, we show that this approach outperforms previously proposed models on the NORB object recognition dataset.

This paper carries on research surrounding the influences produced by dimensionality reduction on machine learning classification effect. Firstly, paper constructs the analysis architecture of data dimension reduction classification, combines the two different unsupervised dimension reduction methods, locally linear embedding (LLE) and principal component analysis (PCA) with the five machine learning classification methods: Gradient Boosting Decision Tree (GBDT), Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression. And then uses the handwritten digital identification dataset to analyze the classification performance of these five classification methods on different dimension datasets by different dimensionality reduction methods. The analysis shows that using the appropriate dimensionality reduction method for dimensionality reduction classification can effectively improve the classification accuracy; the dimensionality reduction classification effect of non-linear dimensionality reduction method is generally better than the linear dimensionality reduction method; different machine learning classification algorithms have significant differences in the sensitivity of dimensions.

Enhanced locality preserving projections using robust path based similarity

In this paper, we discuss dimension reduction methods for 2D visualization of high dimensional clustered data. We propose a two-stage framework for visualizing such data based on dimension reduction methods. In the first stage, we obtain the reduced dimensional data by applying a supervised dimension reduction method such as linear discriminant analysis which preserves the original cluster structure in terms of its criteria. The resulting optimal reduced dimension depends on the optimization criteria and is often larger than 2. In the second stage, the dimension is further reduced to 2 for visualization purposes by another dimension reduction method such as principal component analysis. The role of the second-stage is to minimize the loss of information due to reducing the dimension all the way to 2. Using this framework, we propose several two-stage methods, and present their theoretical characteristics as well as experimental comparisons on both artificial and real-world text data sets.

In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification.

Lorentzian geometry is a subject of mathematics and has famous applications in physics, especially in relativity theory. This geometry has interesting features, e.g. one axis has a negative sign in metric definition (time axis). In this study, we try to apply Lorentzian geometry for feature extraction and dimensionality reduction. We use a Lorentzian Manifold (LM) for face recognition and reduce the dimensionality in this new feature space. We compare results with different feature extraction methods; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). Our experiments show that the best feature extraction method is LM and it produces the best face recognition rates. It is also powerful in dimensionality reduction.

A kernel based nonlinear subspace projection (KNSP) method is proposed for reduction of hyperspectral image dimensionality. This method involves three steps: subspace partition of full data space, feature extraction based on kernel principal component analysis (KPCA) in subspace and feature selection based on class separability criterion. The main merit of the proposed method is that it is more suitable for feature extraction than linear principal component analysis (PCA) and segmented principal component: transformation (SPCT), in particular, when hyperspectral data have nonlinear characteristics. In order to testify the effectiveness of the KNSP method for reduction of hyperspectral image dimensionality, hyperspectral image classification is performed on AVIRIS data. Experimental results show that when the hyperspectral dimensionality is reduced to a few features, the average classification accuracy of the new method is higher than those of PCA and SPCT methods.

Hyperspectral reflectance images of healthy and diseased leaves infected with different isolates of Citrus tristeza virus (CTV) including TRL514, CT30, CT32 and CT11A were collected in the visible and near-infrared region of 400–1000 nm. Average reflectance spectrum was generated from each hyperspectral image individually obtained from 60 healthy and 240 CTV-infected leaves. The spectra were transformed with 15-point Savitzky Golay second derivative. Then principal component analysis was performed on the transformed data in order to reduce the dimension of data. Comparative analysis was performed among supervised classification models, including back-propagation neural network (BPNN), linear discriminant analysis (LDA) and Mahalanobis distance (MD). When the second derivative spectra were analyzed, classifier models including BPNN, LDA and MD can discriminate the healthy and CTV-infected leaves with the highest classification accuracies of 100% in the spectral range of 400–1000 nm and 760–1000 nm. Nine op...