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E-raamat: Data-Variant Kernel Analysis

(Virginia Commonwealth University, Richmond, VA; Purdue University, West Lafayette, IN)
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Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years

This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state.

Data-Variant Kernel Analysis:

  • Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA)
  • Develops group kernel analysis with the distributed databases to compare speed and memory usages
  • Explores the possibility of real-time processes by synthesizing offline and online databases
  • Applies the assembled databases to compare cloud computing environments
  • Examines the prediction of longitudinal data with time-sequential configurations

Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.

List of Figures xiii
List of Tables xix
Preface xxiii
Acknowledgments xxv
Chapter 1 Survey 1(40)
1.1 Introduction of Kernel Analysis
1(1)
1.2 Kernel Offline Learning
2(10)
1.2.1 Choose the Appropriate Kernels
3(3)
1.2.2 Adopt KA into the Traditionally Developed Machine Learning Techniques
6(3)
1.2.3 Structured Database with Kernel
9(3)
1.3 Distributed Database with Kernel
12(4)
1.3.1 Multiple Database Representation
12(1)
1.3.2 Kernel Selections Among Heterogeneous Multiple Databases
13(1)
1.3.3 Multiple Database Representation KA Applications to Distributed Databases
14(2)
1.4 Kernel Online Learning
16(6)
1.4.1 Kernel-Based Online Learning Algorithms
16(1)
1.4.2 Adopt "Online" KA Framework into the Traditionally Developed Machine Learning Techniques
17(4)
1.4.3 Relationship Between Online Learning and Prediction Techniques
21(1)
1.5 Prediction with Kernels
22(4)
1.5.1 Linear Prediction
22(1)
1.5.2 Kalman Filter
23(1)
1.5.3 Finite-State Model
23(1)
1.5.4 Autoregressive Moving Average Model
24(1)
1.5.5 Comparison of Four Models
25(1)
1.6 Future Direction and Conclusion
26(1)
References
26(15)
Chapter 2 Offline Kernel Analysis 41(28)
2.1 Introduction
41(2)
2.2 Kernel Feature Analysis
43(6)
2.2.1 Kernel Basics
43(2)
2.2.2 Kernel Principal Component Analysis (KPCA)
45(1)
2.2.3 Accelerated Kernel Feature Analysis (AKFA)
46(2)
2.2.4 Comparison of the Relevant Kernel Methods
48(1)
2.3 Principal Composite Kernel Feature Analysis (PC-KFA)
49(5)
2.3.1 Kernel Selections
49(3)
2.3.2 Kernel Combinatory Optimization
52(2)
2.4 Experimental Analysis
54(7)
2.4.1 Cancer Image Datasets
54(2)
2.4.2 Kernel Selection
56(2)
2.4.3 Kernel Combination and Reconstruction
58(1)
2.4.4 Kernel Combination and Classification
59(1)
2.4.5 Comparisons of Other Composite Kernel Learning Studies
60(1)
2.4.6 Computation Time
61(1)
2.5 Conclusion
61(1)
References
62(7)
Chapter 3 Group Kernel Feature Analysis 69(28)
3.1 Introduction
69(2)
3.2 Kernel Principal Component Analysis (KPCA)
71(2)
3.3 Kernel Feature Analysis (KFA) for Distributed Databases
73(5)
3.3.1 Extract Data-Dependent Kernels Using KFA
73(2)
3.3.2 Decomposition of Database Through Data Association via Recursively Updating Kernel Matrices
75(3)
3.4 Group Kernel Feature Analysis (GKFA)
78(5)
3.4.1 Composite Kernel: Kernel Combinatory Optimization
79(2)
3.4.2 Multiple Databases Using Composite Kernel
81(2)
3.5 Experimental Results
83(8)
3.5.1 Cancer Databases
83(1)
3.5.2 Optimal Selection of Data-Dependent Kernels
84(1)
3.5.3 Kernel Combinatory Optimization
84(2)
3.5.4 Composite Kernel for Multiple Databases
86(1)
3.5.5 K-NN Classification Evaluation with ROC
87(2)
3.5.6 Comparison of Results with Other Studies on Colonography
89(1)
3.5.7 Computational Speed and Scalability Evaluation of GKFA
90(1)
3.6 Conclusions
91(1)
References
92(5)
Chapter 4 Online Kernel Analysis 97(24)
4.1 Introduction
97(2)
4.2 Kernel Basics: A Brief Review
99(2)
4.2.1 Kernel Principal Component Analysis
99(1)
4.2.2 Kernel Selection
100(1)
4.3 Kernel Adaptation Analysis of PC-KFA
101(1)
4.4 Heterogeneous vs. Homogeneous Data for Online PC-KFA
102(2)
4.4.1 Updating the Gram Matrix of the Online Data
103(1)
4.4.2 Composite Kernel for Online Data
104(1)
4.5 Long-Term Sequential Trajectories with Self-Monitoring
104(3)
4.5.1 Reevaluation of Large Online Data
105(1)
4.5.2 Validation of Decomposing Online Data into Small Chunks
106(1)
4.6 Experimental Results
107(10)
4.6.1 Cancer Datasets
107(1)
4.6.2 Selection of Optimum Kernel and Composite Kernel for Offline Data
108(2)
4.6.3 Selection of Optimum Kernel and Composite Kernel for the New Online Sequences
110(1)
4.6.4 Classification of Heterogeneous Versus Homogeneous Data
111(1)
4.6.5 Online Learning Evaluation of Long-term Sequence
112(4)
4.6.6 Evaluation of Computational Time
116(1)
4.7 Conclusions
117(1)
References
117(4)
Chapter 5 Cloud Kernel Analysis 121(32)
5.1 Introduction
121(2)
5.2 Cloud Environments
123(2)
5.2.1 Server Specifications of Cloud Platforms
123(1)
5.2.2 Cloud Framework of KPCA for AMD
124(1)
5.3 AMD for Cloud Colonography
125(10)
5.3.1 AMD Concept
125(1)
5.3.2 Data Configuration of AMD
126(3)
5.3.3 Implementation of AMD for Two Cloud Cases
129(3)
5.3.4 Parallelization of AMD
132(3)
5.4 Classification Evaluation of Cloud Colonography
135(5)
5.4.1 Databases with Classification Criteria
135(2)
5.4.2 Classification Results
137(3)
5.5 Cloud Computing Performance
140(6)
5.5.1 Cloud Computing Setting with Cancer Databases
140(2)
5.5.2 Computation Time
142(2)
5.5.3 Memory Usage
144(1)
5.5.4 Running Cost
145(1)
5.5.5 Parallelization
145(1)
5.6 Conclusions
146(1)
References
147(6)
Chapter 6 Predictive Kernel Analysis 153(32)
6.1 Introduction
153(1)
6.2 Kernel Basics
154(3)
6.2.1 KPCA and AKFA
155(2)
6.3 Stationary Data Training
157(3)
6.3.1 Kernel Selection
157(2)
6.3.2 Composite Kernel: Kernel Combinatory Optimization
159(1)
6.4 Longitudinal Nonstationary Data with Anomaly Normal Detection
160(3)
6.4.1 Updating the Gram Matrix Based on Nonstationary Longitudinal Data
160(2)
6.4.2 Composite Kernel for Nonstationary Data
162(1)
6.5 Longitudinal Sequential Trajectories for Anomaly Detection and Prediction
163(6)
6.5.1 Anomaly Detection of Nonstationary Small Chunks Datasets
164(3)
6.5.2 Anomaly Prediction of Long-Time Sequential Trajectories
167(2)
6.6 Classification Results
169(6)
6.6.1 Cancer Datasets
169(1)
6.6.2 Selection of Optimum Kernel and Composite Kernel for Stationary Data
170(2)
6.6.3 Comparisons with Other Kernel Learning Methods
172(2)
6.6.4 Anomaly Detection for the Nonstationary Data
174(1)
6.7 Longitudinal Prediction Results
175(5)
6.7.1 Large Nonstationary Sequential dataset for Anomaly Detection
175(3)
6.7.2 Time Horizontal Prediction for Risk Factor Analysis of Anomaly Long-Time Sequential Trajectories
178(1)
6.7.3 Computational Time for Complexity Evaluation
179(1)
6.8 Conclusions
180(1)
References
181(4)
Chapter 7 Conclusion 185(4)
Appendix A 189(6)
Appendix B Representative Matiab codes 195(20)
B.1 Accelerated Kernel Feature Analysis
196(2)
B.2 Experimental Evaluations
198(3)
B.3 Group Kernel Analysis
201(5)
B.4 Online Composite Kernel Analysis
206(2)
B.5 Online Data Sequences Control
208(1)
B.6 Alignment Factor
209(1)
B.7 Cloud Kernel Analysis
210(1)
B.8 Plot Computation Time
211(1)
B.9 Parallelization
212(3)
Index 215
YUICHI MOTAI, Ph.D., is an Associate Professor of Electrical and Computer Engineering at the Virginia Commonwealth University, Richmond, Virginia. He received his Ph.D. with the Robot Vision Laboratory in the School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana in 2002.
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