Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Parallel Computing of Kernel Density Estimates with MPI
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Clustering
ACM Computing Surveys (CSUR)
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This paper deals with dimensionality and sample length reduction applied to the tasks of exploratory data analysis. Proposed technique relies on distance preserving linear transformation of given dataset to the lower dimensionality feature space. Coefficients of feature transformation matrix are found using Fast Simulated Annealing - an algorithm inspired by physical annealing of solids. Furthermore the elimination or weighting of data elements which, as an effect of above mentioned transformation, were moved significantly from the rest of the dataset can be performed. Presented method was positively verified in routines of clustering, classification and outlier detection. It ensures proper efficiency of those procedures in compact feature space and with reduced data sample length at the same time.