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Kernel Change-point Analysis
2008
Neural Information Processing Systems
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We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. ...
Change-point analysis of an unlabelled sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating the change-point ...
Kernel change-point analysis Now, we may apply the strategy described before (cf. Figure 1 ) to obtain the main building block of our test statistic for change-point analysis. ...
dblp:conf/nips/HarchaouiBM08
fatcat:mwjvx7ggbbhhtflw3re4njshym
Selection of Spatial Regression Model Using Point Pattern Analysis
2014
Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography
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To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. ...
When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following ...
change as kernel bandwidth changes. ...
doi:10.7848/ksgpc.2014.32.3.225
fatcat:ftehngpdsrhsneczdlqaaq6jya
Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations
2011
IEEE Transactions on Image Processing
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Three examples show the very successful application of kernel MAF/MNF analysis to: 1) change detection in DLR 3K camera data recorded 0.7 s apart over a busy motorway, 2) change detection in hyperspectral ...
This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. ...
We see that both types of kernel analysis emphasize change and that unlike kernel PCA, kernel MAF analysis seems to focus on the most conspicuous changes and that it gives a very strong discrimination ...
doi:10.1109/tip.2010.2076296
pmid:20840897
fatcat:n32uhbrft5cqdi5oj7wkiojxoq
Kernel methods in orthogonalization of multi-and hypervariate data
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
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A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly, and applied to change detection in remotely sensed hyperspectral image (HyMap) data. ...
An example shows the successful application of kernel MAF analysis to change detection in HyMap data covering a small agricultural area near Lake Waging-Taching, ...
[5] used MAF analysis to detect change in images consisting of simple differences between corresponding spectral bands acquired at two points in time. ...
doi:10.1109/icip.2009.5414469
dblp:conf/icip/Nielsen09
fatcat:fk62gfz6infwffjns4ncvxhuaq
Analysis of Kernel Effects on Optimisation Mismatch in Cache Reconfiguration
2007
2007 International Conference on Field Programmable Logic and Applications
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A cache configuration analysis methodology for fast searching of the design space is also explained and was used to find relevant changes due to kernel interference. ...
Considering kernel operations changes the subset of cache configurations that would be chosen for dynamic cache switching and also the decisions on when to cache switch. ...
CONCLUSIONS Kernel effects were found to change the optimisation mismatch analysis and consequently change the subset of cache configurations that are chosen and also the decisions on when to cache switch ...
doi:10.1109/fpl.2007.4380732
dblp:conf/fpl/ShieldSM07a
fatcat:uo5qbwm6ajfqveg4ikjplwasze
Secure virtual architecture
2007
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles - SOSP '07
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We have ported the Linux kernel to SVA, treating it as a new architecture, and made only minimal code changes (less than 300 lines of code) to the machine-independent parts of the kernel and device drivers ...
SVA aims to enforce fine-grain (object level) memory safety, control-flow integrity, type safety for a subset of objects, and sound analysis. ...
Changes to Improve Analysis We made several changes to the kernel source code to help improve the precision of the analysis, including the changes to eliminate unanalyzable int-to-pointer casts as explained ...
doi:10.1145/1294261.1294295
dblp:conf/sosp/CriswellLDA07
fatcat:pwuc7r7wbbdr5c634g3nsnc7fa
Secure virtual architecture
2007
ACM SIGOPS Operating Systems Review
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We have ported the Linux kernel to SVA, treating it as a new architecture, and made only minimal code changes (less than 300 lines of code) to the machine-independent parts of the kernel and device drivers ...
SVA aims to enforce fine-grain (object level) memory safety, control-flow integrity, type safety for a subset of objects, and sound analysis. ...
Changes to Improve Analysis We made several changes to the kernel source code to help improve the precision of the analysis, including the changes to eliminate unanalyzable int-to-pointer casts as explained ...
doi:10.1145/1323293.1294295
fatcat:735u6minbvdk3bipx53qhavhri
Patch (1) Considered Harmful
2005
USENIX Workshop on Hot Topics in Operating Systems
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It requires nontrivial effort to maintain such a crosscutting patch, even across minor kernel upgrades due to the variability of the kernel proper. ...
Developers of such systems often start with a mainline kernel from kernel.org and then apply patches for their application domain. ...
Our preliminary analysis of patches that update existing kernel functionality shows that the bulk fall into the intraprocedural and intermodule changes categories with very few intramodule changes category ...
dblp:conf/hotos/FiuczynskiGCW05
fatcat:ormxnqcnbvfb7imqtua4bct7ee
Kernel principal component analysis for change detection
2008
Image and Signal Processing for Remote Sensing XIV
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In this paper a kernel version of PCA is used to carry out the analysis. ...
Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. ...
Inspired by the success of ordinary canonical correlation analysis (CCA) to multivariate change detection [13] [14] [15] and normalization over time 16, 17 the application of kernel CCA to these subjects ...
doi:10.1117/12.800141
fatcat:asegpsr5nney7chj5tmh45xqyi
Metabolite Profiling and Classification of Developing Styrax tonkinensis Kernels
2020
Metabolites
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Our results described the change in major metabolites and main metabolic processes during S. tonkinensis kernel development and provided a variety of bases for seed applications as biofuel or medicine. ...
Methods: During S. tonkinensis kernel development, we collected samples from four time points for metabolite profiling and classification through gas chromatography-mass spectrometry and liquid chromatography-mass ...
Acknowledgments: We thank Xiaoyue Ji from Advanced Analysis Testing Center, Nanjing Forestry University for technical assistance.
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/metabo10010021
pmid:31906354
pmcid:PMC7022597
fatcat:nprcsnnft5dldej7ilpqufwqry
Corrected kernel principal component analysis for model structural change detection
[article]
2023
arXiv
pre-print
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This paper develops a method to detect model structural changes by applying a Corrected Kernel Principal Component Analysis (CKPCA) to construct the so-called central distribution deviation subspaces. ...
The numerical studies on synthetic and real data sets suggest that the dimension reduction versions of existing methods for change point detection and clustering significantly improve the performances ...
Limit theorems in change-point analysis. John Wiley & Sons. Enikeeva, F., Z. Harchaoui, et al. (2019). High-dimensional change-point detection under sparse alternatives. ...
arXiv:2307.07827v1
fatcat:feek7qu2pzbhjkaapiz75kyozy
A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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This paper presents a kernel-based approach for the change detection of remote sensing images. ...
Experimental results show that overall accuracy of the algorithm is 98.9 %, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7& ...
A key point of kernel method is that ) , ( q p D S must be computed in the input space. ...
doi:10.5194/isprs-archives-xli-b7-999-2016
fatcat:bmqbhvyq3bd7rpzkp2axteypra
3D Roughness Measurement of Failure Surface in CFA Pile Samples Using Three-Dimensional Laser Scanning
2021
Applied Sciences
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The three-dimensional point cloud of each failure surface was analyzed by a plane to points histogram (P2PH) method and a roughness detection method by kernel proposed in this paper. ...
Therefore, high resolution of point clouds can be obtained to simulate the failure surfaces of three samples. ...
analysis of changing the kernel radius. ...
doi:10.3390/app11062713
fatcat:55nw3yysondevbul7w5ef75szm
urvey of HIS Method in Image Change Detective Algorithms Using Kernel Techniques
2013
International Journal of Electronics and Electrical Engineering
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Studied methods in this article including differing image, the image processing, the principal component analysis, identifying changes of linear chronogram, correlation coefficient of the possibility, ...
Abstract-This article is studied changes detection used in hyper spectrum images. Change Detection (CD ), processing of identity and non-dependent studies and spectral changes in the signal. ...
HIS data cube
Figure 3 . 3 Analysis of cube to cube changes
Figure 4 . 4 Analysis of band to band changes IV. ...
doi:10.12720/ijeee.1.3.206-212
fatcat:fpk2wkojbbapblpyny5ejmjavy
Causality in Scale Space as an Approach to Change Detection
2012
PLoS ONE
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Furthermore this technique enables us to obtain a retrospective reliable interval estimate of the time of a change point rather than a point estimate. ...
Our method reliably detects change points even with little to no knowledge about the relevant scale of the problem. ...
point analysis. ...
doi:10.1371/journal.pone.0052253
pmid:23300626
pmcid:PMC3531480
fatcat:rc45a45dufgj3ahrzcqfrajpdi
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