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» Supervised dimensionality reduction using mixture models
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ICPR
2008
IEEE
16 years 28 days ago
Component-wise parameter smoothing for learning mixture models
In this paper, we propose a novel component-wise smoothing algorithm that constructs a hierarchy (or family) of smoothened log-likelihood surfaces. Our approach first smoothens th...
Bala Rajaratnam, Chandan K. Reddy
KDD
2003
ACM
127views Data Mining» more  KDD 2003»
16 years 4 days ago
Experiments with random projections for machine learning
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...
Dmitriy Fradkin, David Madigan
MICAI
2009
Springer
15 years 4 months ago
Leukocyte Recognition Using EM-Algorithm
This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern...
Mario Chirinos Colunga, Oscar Sánchez Siord...
JMLR
2010
119views more  JMLR 2010»
14 years 6 months ago
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dim...
Milos Radovanovic, Alexandros Nanopoulos, Mirjana ...
ICCV
2007
IEEE
16 years 1 months ago
Real-time Body Tracking Using a Gaussian Process Latent Variable Model
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...
Shaobo Hou, Aphrodite Galata, Fabrice Caillette, N...