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CIDM
2007
IEEE
14 years 2 months ago
Scalable Clustering for Large High-Dimensional Data Based on Data Summarization
Clustering large data sets with high dimensionality is a challenging data-mining task. This paper presents a framework to perform such a task efficiently. It is based on the notio...
Ying Lai, Ratko Orlandic, Wai Gen Yee, Sachin Kulk...
ICASSP
2011
IEEE
12 years 11 months ago
Using clustering comparison measures for speaker recognition
Recent results seem to cast some doubt over the assumption that improvements in fused recognition accuracy for speaker recognition systems based on different acoustic features are...
Jia Min Karen Kua, Julien Epps, Mohaddeseh Nosrati...
WWW
2008
ACM
13 years 7 months ago
A Novelty-based Clustering Method for On-line Documents
In this paper, we describe a document clustering method called noveltybased document clustering. This method clusters documents based on similarity and novelty. The method assigns...
Sophoin Khy, Yoshiharu Ishikawa, Hiroyuki Kitagawa
ICMCS
2006
IEEE
105views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Entropy and Memory Constrained Vector Quantization with Separability Based Feature Selection
An iterative model selection algorithm is proposed. The algorithm seeks relevant features and an optimal number of codewords (or codebook size) as part of the optimization. We use...
Sangho Yoon, Robert M. Gray
NIPS
2008
13 years 9 months ago
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh