Detecting Shilling Attacks with Automatic Features from Multiple Views

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

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An efficient conserved region detection method for multiple protein sequences using principal component analysis and wavelet transform

Pattern Recognition Letters ◽

10.1016/j.patrec.2007.11.013 ◽

2008 ◽

Vol 29 (5) ◽

pp. 616-628 ◽

Cited By ~ 2

Author(s):

Chieh-Yuan Tsai ◽

Chuang-Cheng Chiu

Keyword(s):

Principal Component Analysis ◽

Wavelet Transform ◽

Detection Method ◽

Protein Sequences ◽

Principal Component ◽

Component Analysis ◽

Region Detection ◽

Multiple Protein ◽

Conserved Region

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Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Proceedings ◽

10.3390/proceedings2130989 ◽

2018 ◽

Vol 2 (13) ◽

pp. 989 ◽

Cited By ~ 1

Author(s):

Agus Budi Dharmawan ◽

Gregor Scholz ◽

Shinta Mariana ◽

Philipp Hörmann ◽

Igi Ardiyanto ◽

...

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Artificial Neural Networks ◽

Light Emitting Diode ◽

Principal Component ◽

Component Analysis ◽

Detection Accuracy ◽

Cell Detection ◽

Artificial Neural ◽

Feature Values

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

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Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis

Infrared Physics & Technology ◽

10.1016/j.infrared.2015.01.017 ◽

2015 ◽

Vol 69 ◽

pp. 123-135 ◽

Cited By ~ 49

Author(s):

Chuanyun Wang ◽

Shiyin Qin

Keyword(s):

Principal Component Analysis ◽

Detection Method ◽

Principal Component ◽

Component Analysis ◽

Small Target ◽

Adaptive Detection ◽

Robust Principal Component Analysis ◽

Infrared Small Target

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A Novel Edge Effect Detection Method for Real-Time Cellular Analyzer Using Functional Principal Component Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2019.2903094 ◽

2019 ◽

pp. 1-1

Author(s):

Qian Guo ◽

Tianhong Pan ◽

Shan Chen ◽

Xiaobo Zou ◽

Dorothy Yu Huang

Keyword(s):

Principal Component Analysis ◽

Real Time ◽

Edge Effect ◽

Detection Method ◽

Principal Component ◽

Component Analysis ◽

Functional Principal Component Analysis ◽

Functional Principal Component

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Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

The Scientific World JOURNAL ◽

10.1155/2014/419308 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Shengkun Xie ◽

Sridhar Krishnan

Keyword(s):

Principal Component Analysis ◽

Single Channel ◽

Principal Component ◽

Component Analysis ◽

Classification Performance ◽

Detection Methods ◽

Moving Window ◽

Feature Extraction Method ◽

Epilepsy Diagnosis ◽

Dynamic Principal Component Analysis

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

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CORRIDOR-SCENE CLASSIFYING METHODS FOR MOBILE ROBOT BASED ON MULTI-SONAR-SENSOR INFORMATION FUSION

International Journal of Information Acquisition ◽

10.1142/s0219878907001125 ◽

2007 ◽

Vol 04 (01) ◽

pp. 15-26 ◽

Cited By ~ 3

Author(s):

XIUQING WANG ◽

ZENG-GUANG HOU ◽

LONG CHENG ◽

MIN TAN ◽

FEI ZHU

Keyword(s):

Principal Component Analysis ◽

Mobile Robot ◽

Principal Component ◽

Component Analysis ◽

Experimental Results ◽

Scene Analysis ◽

Kernel Principal Component Analysis ◽

Kernel Pca ◽

Sonar Sensor ◽

Pca Method

The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A new scene analysis method using kernel principal component analysis (kernel-PCA) for mobile robot based on multi-sonar-ranger data fusion is put forward. The principle of classification by principal component analysis (PCA), kernel-PCA, and the BP neural network (NN) approach to extract the eigenvectors which have the largest k eigenvalues are introduced briefly. Next the details of PCA, kernel-PCA and the BP NN method applied in the corridor scene analysis and classification for the mobile robots based on sonar data are discussed and the experimental results of those methods are given. In addition, a corridor-scene-classifier based on BP NN is discussed. The experimental results using PCA, kernel-PCA and the methods based on BP neural networks (NNs) are compared and the robustness of those methods are also analyzed. Such conclusions are drawn: in corridor scene classification, the kernel-PCA method has advantage over the ordinary PCA, and the approaches based on BP NNs can also get satisfactory results. The robustness of kernel-PCA is better than that of the methods based on BP NNs.

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JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2933 ◽

2012 ◽

Vol 263-266 ◽

pp. 2933-2938 ◽

Cited By ~ 1

Author(s):

Feng Ying He ◽

Shang Ping Zhong ◽

Kai Zhi Chen

Keyword(s):

Principal Component Analysis ◽

Image Classification ◽

Detection Rate ◽

Feature Fusion ◽

Principal Component ◽

Component Analysis ◽

Experimental Results ◽

Jpeg Images

Aiming to the problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarities among them, this study proposed a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarities among features. The study fused complementary features and isolated redundant components by PCA, and finally used RBaggSVM classifier for classification. Experimental results show that this scheme effectively improves the detection rate of steganalysis in JPEG images and achieves faster speed of image classification.

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