69 search results - page 9 / 14 » Unsupervised Feature Selection in High Dimensional Spaces an... |
CORR
2006
Springer
14 years 9 months ago
2006
Springer
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
99
Voted
NIPS
2001
14 years 11 months ago
2001
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
106
click to vote
ISMIR
2005
Springer
15 years 3 months ago
2005
Springer
We describe the MusicMiner system for organizing large collections of music with databionic mining techniques. Low level audio features are extracted from the raw audio data on sh...
KDD
2007
ACM
15 years 10 months ago
2007
ACM
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
161
click to vote
SDM
2011
SIAM
14 years 9 days ago
2011
SIAM
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...