Model Selection Techniques and Merging Rules for Range Data Segmentation Algorithms

2000 ◽  
Vol 80 (2) ◽  
pp. 215-245 ◽  
Author(s):  
Kishore Bubna ◽  
Charles V. Stewart
Keyword(s):  
Model Selection ◽  
Range Data ◽  
Data Segmentation ◽  
Segmentation Algorithms ◽  
Range Data Segmentation

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Robust segmentation of visual data using ranked unbiased scale estimate

Robotica ◽  
1999 ◽  
Vol 17 (6) ◽  
pp. 649-660 ◽  
Author(s):  
Alireza Bab-Hadiashar ◽  
David Suter
Keyword(s):  
Statistical Model ◽  
Model Fitting ◽  
Estimation Method ◽  
Image Motion ◽  
Range Data ◽  
Data Segmentation ◽  
Robust Segmentation ◽  
Optimization Routine ◽  
Complex Optimization ◽  
Scale Estimate

A method of data segmentation, based upon robust least K-th order statistical model fitting (LKS), is proposed and applied to image motion and range data segmentation. The estimation method differs from other approaches using versions of LKS in a number of important ways. Firstly, the value of K is not determined by a complex optimization routine. Secondly, having chosen a fit, the estimation of scale of the noise is not based upon the K-th order statistic of the residuals. Other aspects of the full segmentation scheme include the use of segment contiguity to: (a) reduce the number of random sample fits used in the LKS stage, and (b) to “fill-in” holes caused by isolated miss-classified data.

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