Efficient Dimensionality Reduction Technique Research Articles

Consensus and discriminative non-negative matrix factorization for multi-view unsupervised feature selection

Multi-view unsupervised feature selection (MUFS) has been proven to be an efficient dimensionality reduction technique for multi-view data. Existing methods have two main challenges: (1) The consistency information from different views is not fully exploited. (2) The cluster structure of the original data is not well utilized. To settle these problems, we propose a novel consensus and discriminative non-negative matrix factorization (CDNMF) for multi-view unsupervised feature selection. Specifically, CDNMF obtains a robust low-dimensional latent space by NMF with an ℓ2,1-norm constraint. To select more discriminative features, we further impose a sparsity constraint on the learned latent features. Moreover, CDNMF performs k-means clustering on all views separately to obtain the pseudo-labels of each view, which are used to guide the learning of consensus information among views. To solve our model, we develop an efficient iterative optimization algorithm. Extensive experimental results on ten benchmark datasets demonstrate that the proposed significantly outperforms several existing feature selection methods in clustering tasks.

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.

Accelerating a Geometrical Approximated PCA Algorithm Using AVX2 and CUDA

Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representation. Projection Pursuit (PP) based algorithms were shown to be efficient solutions for performing dimensionality reduction on large datasets by searching low-dimensional projections of the data where meaningful structures are exposed. However, PP faces computational difficulties in dealing with very large datasets—which are common in hyperspectral imaging, thus raising the challenge for implementing such algorithms using the latest High Performance Computing approaches. In this paper, a PP-based geometrical approximated Principal Component Analysis algorithm (gaPCA) for hyperspectral image analysis is implemented and assessed on multi-core Central Processing Units (CPUs), Graphics Processing Units (GPUs) and multi-core CPUs using Single Instruction, Multiple Data (SIMD) AVX2 (Advanced Vector eXtensions) intrinsics, which provide significant improvements in performance and energy usage over the single-core implementation. Thus, this paper presents a cross-platform and cross-language perspective, having several implementations of the gaPCA algorithm in Matlab, Python, C++ and GPU implementations based on NVIDIA Compute Unified Device Architecture (CUDA). The evaluation of the proposed solutions is performed with respect to the execution time and energy consumption. The experimental evaluation has shown not only the advantage of using CUDA programming in implementing the gaPCA algorithm on a GPU in terms of performance and energy consumption, but also significant benefits in implementing it on the multi-core CPU using AVX2 intrinsics.

Online action recognition from RGB-D cameras based on reduced basis decomposition

Human action recognition from RGB-D cameras has recently become one of the major fields of research. While accuracy improvement was given more importance in previous action/gesture recognition methods, there are opportunities to work on improving the computational efficiency too. This paper introduces an efficient dimensionality reduction technique and classification mechanism to recognize actions from depth motion map features. For our proposed work, a recently introduced technique called reduced basis decomposition (RBD) is employed, which manages faster dimensional reduction with its unique mechanism of generating compressed basis vectors. The RBD has an offline error-determination and an online approximation mechanism, and it is faster than PCA/SVD. For classification, this paper employs a Probabilistic Collaborative Representation Classifier (Pro-CRC). The recommended classifier works based on probability in connection with $${l_2}$$-regularization. The combined effect of the methods above helps in achieving the state-of-the-art efficiency. In the standard protocol tests carried out in the MSR-Action3D dataset, our proposed method achieved a considerable accuracy of 91.7% which is better than the currently efficient method. Further, our proposed method also proved its effectiveness in the challenging, subject-generic test with a reported accuracy of 89.64% and an average accuracy of 85.70% in the cross fixed tests which included 252 combinations of all the subjects without repetition.

AN EFFICIENT CLASSIFICATIONS MODEL FOR BREAST CANCER PREDICTION BASED ON DIMENSIONALITY REDUCTION TECHNIQUES

Classification algorithms are efficiently utilized in the area of general medical diagnosis applications in order to identify the disorders in advance. One such disease, breast cancer is the most prevalent and earnest quandary with women in most of the developing countries. Many attempts are made in order to identify this problem with the objective of high precision and better accuracy. In this paper, an attempt is made with the most popular and efficient classification algorithms namely Naive Bayes, Multilayer Perceptron, Radial basis function network, nearest neighbour, Conjunctive rule to amend the efficiency of the detection, accuracy for the breast cancer dataset. As an objective of improving accuracy, an efficient dimensionality reduction technique is incorporated in this work. The performances of these approaches are evaluated using the metrics such as the precision, recall, f-measure, roc, Balanced Classification Rate (BCR), Matthews Correlation Coefficient (MCC) and accuracy. From these measures it is clearly observed that Naive Bayes algorithm is able to achieve high accuracy rate along with minimum error rate when compared to other algorithms. The review can be stretched out to draw the execution of other characterization systems on an extended information set with more particular ascribes to get more exact outcomes.

Feature selection for multi-label classification using neighborhood preservation

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.

Dual-graph regularized non-negative matrix factorization with sparse and orthogonal constraints

Semi-supervised Non-negative Matrix Factorization (NMF) can not only utilize a fraction of label information, but also effectively learn local information of the objectives, such as documents and faces. Semi-supervised NMF is an efficient technique for dimensionality reduction of high dimensional data. In this paper, we propose a novel semi-supervised NMF, called Dual-graph regularized Non-negative Matrix Factorization with Sparse and Orthogonal constraints (SODNMF). Dual-graph model is added into semi-supervised NMF, and the manifold structures of the data space and the feature space are taken into account simultaneously. In addition, the sparse constraint is used in SODNMF, which can simplify the calculation and accelerate the processing speed. The most important is that SODNMF makes use of bi-orthogonal constraints, which can avoid the non-correspondence between images and basic vectors. Therefore, it can effectively enhance the discrimination and the exclusivity of clustering, and improve the clustering performance. We give the objective function, the iterative updating rules and the convergence proof. Empirical experiments demonstrate encouraging results of our novel algorithm in comparison to four algorithms within some state-of-the-art algorithms through a set of evaluations based on three real datasets.

An Intelligent Two Phase Fuzzy Decision Tree Based Clustering Model for Design of Computer Aided Detection/Diagnosis (CADe/CADx) System

Automated computer aided detection/diagnosis (CADe/CADx) system plays a key role in decision making process and work as a recommender system for researchers. Nowadays, CADe/CADx systems are getting popular due to their strong ability to extract complex relations present in unprocessed data set. In this research article, we present an intelligent two phase classification model for the design of CADe/CADx system. In order to design an intelligent CAD system, the primary challenge lies in identifying important attributes. The presence of irrelevant and redundant attributes in the data can have adverse impact over classifier accuracy. An efficient dimensionality reduction technique aims at achieving lower computational cost with reduced storage requirement by choosing problem specific relevant or significant attributes. The secondary challenge is to provide unambiguous and comprehensible rule base for accurate predictions. The contribution of work can be stated twofold: first, to attain reasonably good classification accuracy with possible speed up, linear discriminant analysis and some popular correlation coefficients (Fisher, Phi and Point bi-serial) are being used to identify significant attributes. Second, to generate comprehensible and understandable rule set a fuzzy decision tree based clustering approach is used. The performance of proposed model is verified on twelve famous UCI data sets.

Unsupervised feature selection via maximum projection and minimum redundancy

Dimensionality reduction is an important and challenging task in machine learning and data mining. It can facilitate data clustering, classification and information retrieval. As an efficient technique for dimensionality reduction, feature selection is about finding a small feature subset preserving the most relevant information. In this paper, we propose a new criterion, called maximum projection and minimum redundancy feature selection, to address unsupervised learning scenarios. First, the feature selection is formalized with the use of the projection matrices and then characterized equivalently as a matrix factorization problem. Second, an iterative update algorithm and a greedy algorithm are proposed to tackle this problem. Third, kernel techniques are considered and the corresponding algorithm is also put forward. Finally, the proposed algorithms are compared with four state-of-the-art feature selection methods. Experimental results reported for six publicly datasets demonstrate the superiority of the proposed algorithms.

An HOG-LGBPHS human detector with dimensionality reduction by PLS

By combining Histogram of Oriented Gradient (HOG), which is based on evaluating well-normalized local histograms of image gradient orientations in a dense grid, with Local Gabor Binary Pattern Histogram Sequence (LGBPHS), which concatenate the histograms of all the local regions of all the local Gabor magnitude binary pattern maps, as a feature set, we proposed a novel human detection feature. We employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, to project the feature onto a much lower dimensional subspace (9 dimensions, reduced from the original over 12000). We test the new feature in INRIA person dataset by using a linear SVM, and it yields an error rate of 1.35% with a false negatives (FN) rate of 0.46% and a false positive (FP) rate of 0.89%, while the error rate of HOG is 7.11% with a FN rate of 4.09% and a FP rate of 3.02%, and the error rate of LGBPHS is 13.55% with a FN rate of 4.94% and a FP rate of 8.61%.

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties.