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ICML
2003
IEEE
16 years 3 months ago
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
Zhihua Zhang
102
Voted
KDD
2004
ACM
103views Data Mining» more  KDD 2004»
16 years 2 months ago
An objective evaluation criterion for clustering
We propose and test an objective criterion for evaluation of clustering performance: How well does a clustering algorithm run on unlabeled data aid a classification algorithm? The...
Arindam Banerjee, John Langford
DCC
2007
IEEE
16 years 2 months ago
Structure induction by lossless graph compression
This work is motivated by the necessity to automate the discovery of structure in vast and evergrowing collection of relational data commonly represented as graphs, for example ge...
Leonid Peshkin
103
Voted
CHI
2010
ACM
15 years 9 months ago
MotionBeam: designing for movement with handheld projectors
In this paper we present a novel interaction metaphor for handheld projectors we label MotionBeam. We detail a number of interaction techniques that utilize the physical movement ...
Karl D. D. Willis, Ivan Poupyrev
150
Voted
CVPR
2009
IEEE
15 years 9 months ago
Manifold Discriminant Analysis
This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image s...
Ruiping Wang, Xilin Chen