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ICCBR
2007
Springer
14 years 4 months ago
Case-Based Reasoning Adaptation for High Dimensional Solution Space
Case-Based Reasoning (CBR) is a methodology that reuses the solutions of previous similar problems to solve new problems. Adaptation is the most difficult stage in the CBR cycle, e...
Ying Zhang, Panos Louvieris, Maria Petrou
ICONIP
2007
13 years 11 months ago
Principal Component Analysis for Sparse High-Dimensional Data
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
Tapani Raiko, Alexander Ilin, Juha Karhunen
ICASSP
2011
IEEE
13 years 1 months ago
Particle algorithms for filtering in high dimensional state spaces: A case study in group object tracking
We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex...
Lyudmila Mihaylova, Avishy Carmi
ICCD
2008
IEEE
117views Hardware» more  ICCD 2008»
14 years 6 months ago
Two dimensional highly associative level-two cache design
High associativity is important for level-two cache designs [9]. Implementing CAM-based Highly Associative Caches (CAM-HAC), however, is both costly in hardware and exhibits poor s...
Chuanjun Zhang, Bing Xue
BMCBI
2007
123views more  BMCBI 2007»
13 years 9 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...