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ML
2010
ACM
193views Machine Learning» more  ML 2010»
9 years 8 months ago
On the eigenvectors of p-Laplacian
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natur...
Dijun Luo, Heng Huang, Chris H. Q. Ding, Feiping N...
ML
2010
ACM
124views Machine Learning» more  ML 2010»
9 years 11 months ago
Large scale image annotation: learning to rank with joint word-image embeddings
Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method tha...
Jason Weston, Samy Bengio, Nicolas Usunier
INFORMATICALT
2008
97views more  INFORMATICALT 2008»
10 years 1 months ago
On Dimensionality of Embedding Space in Multidimensional Scaling
Multidimensional scaling is a technique for exploratory analysis of multidimensional data widely usable in different applications. By means of this technique the image points in a ...
Julius Zilinskas
NIPS
2004
10 years 2 months ago
Parametric Embedding for Class Visualization
In this paper, we propose a new method, Parametric Embedding (PE), for visualizing the posteriors estimated over a mixture model. PE simultaneously embeds both objects and their c...
Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean S...
ADBIS
2004
Springer
134views Database» more  ADBIS 2004»
10 years 6 months ago
Towards Quadtree-Based Moving Objects Databases
Nowadays, one of the main research issues of great interest is the efficient tracking of mobile objects that enables the effective answering of spatiotemporal queries. This line o...
Katerina Raptopoulou, Michael Vassilakopoulos, Yan...
HUMO
2007
Springer
10 years 7 months ago
Robust Spectral 3D-Bodypart Segmentation Along Time
Abstract. In this paper we present a novel tool for body-part segmentation and tracking in the context of multiple camera systems. Our goal is to produce robust motion cues over ti...
Fabio Cuzzolin, Diana Mateus, Edmond Boyer, Radu H...
CVPR
2009
IEEE
10 years 8 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
ICML
2002
IEEE
11 years 2 months ago
Learning the Kernel Matrix with Semi-Definite Programming
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is perfor...
Gert R. G. Lanckriet, Nello Cristianini, Peter L. ...
ICIP
2007
IEEE
11 years 3 months ago
Unsupervised Modeling of Object Tracks for Fast Anomaly Detection
A key goal of far-field activity analysis is to learn the usual pattern of activity in a scene and to detect statistically anomalous behavior. We propose a method for unsupervised...
Tomas Izo, W. Eric L. Grimson
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