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» Learning from Highly Structured Data by Decomposition
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ICCV
2007
IEEE
15 years 4 months ago
Laplacian PCA and Its Applications
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
Deli Zhao, Zhouchen Lin, Xiaoou Tang
MIR
2006
ACM
141views Multimedia» more  MIR 2006»
15 years 4 months ago
Mining temporal patterns of movement for video content classification
Scalable approaches to video content classification are limited by an inability to automatically generate representations of events ode abstract temporal structure. This paper pre...
Michael Fleischman, Philip DeCamp, Deb Roy
AAAI
2008
15 years 10 days ago
Sparse Projections over Graph
Recent study has shown that canonical algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be obtained from graph based dimensionality ...
Deng Cai, Xiaofei He, Jiawei Han
CACM
2010
161views more  CACM 2010»
14 years 8 months ago
Efficiently searching for similar images
As it becomes increasingly viable to capture, store, and share large amounts of image and video data, automatic image analysis is crucial to managing visual information. Many prob...
Kristen Grauman
SIGMOD
1998
ACM
142views Database» more  SIGMOD 1998»
15 years 2 months ago
Dimensionality Reduction for Similarity Searching in Dynamic Databases
Databases are increasingly being used to store multi-media objects such as maps, images, audio and video. Storage and retrieval of these objects is accomplished using multi-dimens...
Kothuri Venkata Ravi Kanth, Divyakant Agrawal, Amb...