scholarly journals INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

2015 ◽  
Vol II-4/W2 ◽  
pp. 67-71 ◽  
Author(s):  
A. Bhushan ◽  
M. H. Sharker ◽  
H. A. Karimi
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Outlier Detection ◽  
Data Streams ◽  
Credit Card ◽  
Principal Component ◽  
Component Analysis ◽  
Spatiotemporal Data ◽  
Sensor Data ◽  
Detection Methods

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

scholarly journals Real-Time Principal Component Analysis

2020 ◽  
Vol 1 (2) ◽  
pp. 1-36
Author(s):  
Ranak Roy Chowdhury ◽  
Muhammad Abdullah Adnan ◽  
Rajesh K. Gupta
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

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.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
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.

207 Real-time recognition of gesture using vision based analysis and principal component analysis

2006 ◽  
Vol 2006.55 (0) ◽  
pp. 67-68
Author(s):  
Goro OBINATA ◽  
Hiroki KATO ◽  
Kazunori HASE ◽  
Atsushi NAKAYAMA
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis
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