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» On the Optimality of the Dimensionality Reduction Method
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ECCV
2004
Springer
16 years 8 months ago
Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs
In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge...
M. Fatih Demirci, Ali Shokoufandeh, Sven J. Dickin...
ISDA
2010
IEEE
15 years 4 months ago
Feature selection is the ReliefF for multiple instance learning
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
SSD
2005
Springer
122views Database» more  SSD 2005»
15 years 11 months ago
Selectivity Estimation of High Dimensional Window Queries via Clustering
Abstract. Query optimization is an important functionality of modern database systems and often based on estimating the selectivity of queries before actually executing them. Well-...
Christian Böhm, Hans-Peter Kriegel, Peer Kr&o...
ICDM
2002
IEEE
191views Data Mining» more  ICDM 2002»
15 years 11 months ago
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
Inderjit S. Dhillon, Yuqiang Guan, J. Kogan
ICCV
2007
IEEE
16 years 8 months ago
Locally Smooth Metric Learning with Application to Image Retrieval
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
Dit-Yan Yeung, Hong Chang