165 search results - page 31 / 33 » Adaptive dimension reduction for clustering high dimensional... |
EUROSYS
2006
ACM
14 years 3 months ago
2006
ACM
Building content-based search tools for feature-rich data has been a challenging problem because feature-rich data such as audio recordings, digital images, and sensor data are in...
AAAI
2006
13 years 7 months ago
2006
Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various machine learning and pattern classification tasks...
TKDE
2012
11 years 8 months ago
2012
—Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorith...
SIAMMAX
2010
13 years 22 days ago
2010
Linear Discriminant Analysis (LDA) is one of the most popular approaches for feature extraction and dimension reduction to overcome the curse of the dimensionality of the high-dime...
SIGMOD
2002
ACM
14 years 6 months ago
2002
ACM
Dwarf is a highly compressed structure for computing, storing, and querying data cubes. Dwarf identifies prefix and suffix structural redundancies and factors them out by coalesci...