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» Lossy Reduction for Very High Dimensional Data
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IJCAI
1997
13 years 7 months ago
Is Nonparametric Learning Practical in Very High Dimensional Spaces?
Many of the challenges faced by the £eld of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and ...
Gregory Z. Grudic, Peter D. Lawrence
EDBT
2006
ACM
154views Database» more  EDBT 2006»
13 years 10 months ago
Approximation Techniques to Enable Dimensionality Reduction for Voronoi-Based Nearest Neighbor Search
Utilizing spatial index structures on secondary memory for nearest neighbor search in high-dimensional data spaces has been the subject of much research. With the potential to host...
Christoph Brochhaus, Marc Wichterich, Thomas Seidl
ICDE
2007
IEEE
228views Database» more  ICDE 2007»
14 years 21 days ago
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the “curse of dimensionality”. In order to speed up the retrieval efficiency, p...
Xiang Lian, Lei Chen 0002
ICDE
2003
IEEE
193views Database» more  ICDE 2003»
14 years 7 months ago
An Adaptive and Efficient Dimensionality Reduction Algorithm for High-Dimensional Indexing
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well known approach to o...
Hui Jin, Beng Chin Ooi, Heng Tao Shen, Cui Yu, Aoy...
AICCSA
2008
IEEE
276views Hardware» more  AICCSA 2008»
13 years 8 months ago
Effects of dimensionality reduction techniques on time series similarity measurements
Time Series are ubiquitous, hence, similarity search is one of the biggest challenges in the area of mining time series data. This is due to the vast data size, number of sequence...
Ghazi Al-Naymat, Javid Taheri