Generalized linear discriminant analysis based on euclidean norm for gait recognition

Wang, Hao; Fan, Yujie; Fang, Baofu; Dai, Shuanglu

doi:10.1007/s13042-016-0540-0

Cited by 33 publications

(12 citation statements)

References 29 publications

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“…Feature selection Load data in HDFS GDA [26] is used for the problems in multi-class classification. In the feature space some overlaps are obtained among the patterns of different attacks due to huge difference in that particular classes and the scatter among the classes are reduced by using feature transformation mechanism.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

Dahiya,

Srivastva

2019

IJEAT

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Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

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Section: Dimensionality Reductionmentioning

confidence: 99%

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

Dahiya,

Srivastva

2019

IJEAT

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“…Gait recognition methods [3] can be categorized as model-free (appearance-based) [2,[4][5][6][7][8][9][10][11][12] and model-based [13][14][15][16][17][18][19][20][21][22]. Most gait recognition studies are based on model-free approaches that employ the whole motion pattern of the human body.…”

Section: Introductionmentioning

confidence: 99%

“…Most gait recognition studies are based on model-free approaches that employ the whole motion pattern of the human body. Several techniques were proposed to characterize this motion pattern, such as the gait energy image (GEI) [2,5,7,10], which is a spatio-temporal gait representation, GEI region bounded by legs (RBL) [8], human body contours [9], and dense optical flow field [4]. These methods are strongly based on silhouette extraction and therefore are not resistant to changing clothes or carrying luggage.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are strongly based on silhouette extraction and therefore are not resistant to changing clothes or carrying luggage. It is also worth noting that most of them can only achieve correct results from a specific point of view, usually side view [4,5,[7][8][9]16]. The recent research on model-free methods has focused on eliminating these drawbacks [6,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…In the recognition process, various classification techniques are used; most often, they are classical methods such as k-nearest neighbors (kNN) [2,4,10,[14][15][16][17]19,23], multilayer perceptron (MLP) [21], support vector machine [9,16,24] linear discriminant analysis (LDA) [16], and radial basis function neural networks [18]. The main focus in these papers is on developing descriptors that better describe gait features, rather than introducing new classification methods with better recognition ability.…”

Section: Introductionmentioning

confidence: 99%

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Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

Krzeszowski

Wiktorowicz

2020

Sensors

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In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.

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Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector

Wang

et al. 2021

Can J Chem Eng

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As one of the main costs of industrial processes, energy consumption is an essential issue that cannot be ignored in the sustainable development of enterprises. Effective energy efficiency evaluation is important, but also challenging.The conventional method typically uses statistic standard for evaluation. However, infected by the market, the raw materials of an industrial process often fluctuate, which will affect the evaluation standard of energy efficiency. Taking this into account will improve the evaluation performance. To this end, a realtime dynamic energy efficiency evaluation and analysis method based on multi-objective state transition algorithm with reference vector (RV-MOSTA) is proposed to address the energy efficiency evaluation problem. The core of the paper is to realize the dynamic energy efficiency evaluation of multiple indicators considering inlet conditions of industrial processes. Therefore, a systematic set of special indicators are firstly developed for the energy efficiency evaluation. Then, RV-MOSTA is proposed to determine the evaluation standard, which considers the influence of imported inlet conditions and the optimization of multiple evaluation indicators. Furthermore, a degeneration diagnosis method by means of linear discriminant analysis (LDA) can identify the key variables that lead to energy efficiency degeneration. The proposed method is verified through numerical example and an industrial hydrocracking process.

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Generalized linear discriminant analysis based on euclidean norm for gait recognition

Cited by 33 publications

References 29 publications

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector

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