Low-Rank Preserving Projections.

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.

Enhanced locality preserving projections using robust path based similarity

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.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space

Objectives: The article proposes enhancements to the Generalized Autoencoder (GAE) framework for Dimensionality Reduction (DR).The first objective is to improve the reconstruction process so that recognition accuracy increases. The second objective is to embed class label information into the framework. This turns GAE into a supervised method with better interclass discrimination. Method: Two improvements to the GAE are proposed: (i) an adjustment to reconstruction weights to capture neighbourhood relations in high-dimensional spaces effectively, and (ii) a strategy that incorporates class label information directly into the reconstruction set. The approach is evaluated using standard benchmark datasets for face recognition and handwritten digit classification. The approach is evaluated on eight benchmark datasets: four standard face databases (ORL, AR, UMIST, YaleB) and four handwritten digit databases (MNIST, Gujarati, Devnagari, Bangla). For a fair comparison, recognition performance is studied under varying dimensionality of the reduced feature space, different neighborhood sizes. Findings: Experimental results show that the modified GAE significantly improves recognition performance compared to the original GAE by 1-5%. The supervised version demonstrates stronger discriminative power across all datasets with better recognition rates by 3-7%. Novelty: The proposed approach adds class label information into the reconstruction set, turning GAE into a supervised framework. At the same time, it refines the weight adjustment process. Together, these two changes improve both accuracy and discrimination in dimensionality reduction tasks. Keywords: Dimensionality Reduction, Auto Encoder, Laplacian EigenMaps, Locally Linear Embeddings, Locality Preserving Projection, Neighbourhood Preserving Projection

UAVs (unmanned aerial vehicles) have been widely used in many fields, where they need to be detected and controlled. Small-sample UAV recognition requires an effective detecting and recognition method. When identifying a UAV target using the backward propagation (BP) neural network, fully connected neurons of BP neural network and the high-dimensional input features will generate too many weights for training, induce complex network structure, and poor recognition performance. In this paper, a novel recognition method based on non-negative matrix factorization (NMF) with sparseness constraint feature dimension reduction and BP neural network is proposed for the above difficulties. The Edgeboxes are used for candidate regions and Log-Gabor features are extracted in candidate target regions. In order to avoid the complexity of the matrix operation with the high-dimensional Log-Gabor features, preprocessing for feature reduction by downsampling is adopted, which makes the NMF fast and the feature discriminative. The classifier is trained by neural network with the feature of dimension reduction. The experimental results show that the method is better than the traditional methods of dimension reduction, such as PCA (principal component analysis), FLD (Fisher linear discrimination), LPP (locality preserving projection), and KLPP (kernel locality preserving projection), and can identify the UAV target quickly and accurately.

Locality-preserving projection (LPP) is a promising manifold-based dimensionality reduction and linear feature extraction method for face recognition. However, there exist two main issues in traditional LPP algorithm. LPP does not utilize the class label information at the training stage and its performance will be affected for classification tasks. In addition, LPP often suffers from small sample size (3S) problem, which occurs when the dimension of input pattern space is greater than the number of training samples. Under this situation, LPP fails to work. To overcome these two limitations, this paper presents a novel supervised regularization LPP (SRLPP) approach based on a supervised graph and a new regularization strategy. It theoretically proves that regularization matrix [Formula: see text] approaches to the original one as the regularized parameter tends to zero. The proposed SRLPP method is subsequently applied to face recognition. The experiments are conducted on two publicly available face databases, namely ORL database and FERET database. Compared with some existing LDA-based and LPP-based linear feature extraction approaches, experimental results show that our SRLPP approach gives superior performance.

Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimension reduction algorithms, including principal component analysis (PCA), local preserving projections (LPP), neighborhood preserving embedding (NPE), and linear discriminant analysis (LDA), were used to reduce hyperspectral dimensions, and their classification performances on the algae concentration levels in water samples using hyperspectral imaging were compared. The LPP model showed satisfactory classification accuracy of 94.296 %, which was superior to the results based on reducing spectral dimensions with LDA (94.118 %), NPE (93.353 %), and PCA (90.588 %). The results demonstrated the potential of hyperspectral imaging coupled with dimension reduction methods in classifying water bodies with different algae concentration levels.

Dimensionality reduction technologies are very important for pattern representation and recognition. Among them, locality preserving projection (LPP) is a manifold dimensionality reduction scheme and has been successfully applied to face recognition. However, LPP is an unsupervised linear approach, its performance will degrade for classification tasks. Especially, when the dimension of input space is greater than the number of training data, singularity problem will occur and LPP cannot be implemented directly. To tackle the draw backs of LPP algorithm, this paper proposes a novel regularized LPP(RLPP) approach using supervised graph and regularization technique. The proposed RLPP method has been tested and evaluated with two public available databases, namely ORL and FERET databases. Experimental results show that the proposed RLPP approach surpasses Laplacianface and Direct-LPP (DLPP) methods.

Local appearance-based methods have been proposed recently for face recognition. We analyze the effects of different dimension reduction and normalization methods on local appearance-based face recognition in this paper. Each image is divided into equal sized blocks and six different dimension reduction methods are implemented for each block separately to create local visual feature vectors. On these local features, several normalization methods are applied in an attempt to eliminate the changes in lighting conditions and contrast differences among blocks of different face images. The experimental results show the improvements in recognition rates due to the effects of dimension reduction and normalization for three different classifiers. Usage of trainable dimension reduction methods instead of DCT and a new normalization method in our work (within-block normalization as referred in this paper) are two factors that makes difference from previous works in literature. The best performance is achieved using a block size of 16times16, performing dimension reduction using approximate pairwise accuracy criterion (aPAC) and applying within-block mean and variance normalization.

Compared to vector based manifold learning methods, image based methods can reduce the complexity of algorithm, avoid the small sample size problem and give more spatial structural information of image. Two-dimensionality Locality Discriminat Projections (2D-LDP) is proposed, which an effective dimensionality reduction method and benefits from three parts, i.e., Locality Preserving Projections (LPP) algorithm, image based projection and discriminant analysis. In this paper, we apply 2DLPP to plant leaf classification. 2D-LDP can detect the intrinsic class-relationships between the leaf images by incorporating both class label information and neighborhood information. The Experimental results show that 2D-DLPP has better classifying performance than other methods.KeywordsLocality Preserving Projections (LPP)Two-dimensionality Locality Discriminat Projections (2D-LDP)Plant leaf Classification

Exploring dimensionality reduction of EEG features in motor imagery task classification

Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low-rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the submanifold information of the original data set, multimanifold learning is effective in enhancing the discriminant ability of the processed data set. In addition, due to the ability of preserving the spatial neighborhood structure information, the tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above-mentioned analysis, a novel tensor-based low-rank graph with multimanifold regularization (T-LGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In the T-LGMR, a low-rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability, and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art approaches.

Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and subsequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.

The problems of high dimensional feature vectors lead to large data calculating and time consuming in Scale Invariant Feature Transform (SIFT) feature matching. This paper combines the SIFT and Locality Preserving Projection (LPP) as the LPP-SIFT algorithm. First, use the SIFT algorithm to extract feature point vectors, then by the linear LPP dimensionality reduction method, reduce the amount of data. Use the Euclidean distance similarity criteria to achieve the coarse matching feature point vectors. Then re-match each pair of the match points by the way of the Gaussian neighborhood weighted average to get rid of the mismatch points. The experimental results show that the improved LPP-SIFT algorithm can reduce the amount of data, enhance the real-time and has a high matching accuracy and matching efficiency.