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» Semi-Supervised Dimensionality Reduction
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CVPR
2007
IEEE
16 years 5 months ago
Optimal Dimensionality Discriminant Analysis and Its Application to Image Recognition
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, Linear Discriminant Analysis (LDA) is one of the most po...
Feiping Nie, Shiming Xiang, Yangqiu Song, Changshu...
113
Voted
VC
2010
177views more  VC 2010»
15 years 2 months ago
Color-to-gray conversion using ISOMAP
In this paper we present a new algorithm to transform an RGB color image to a grayscale image. We propose using non-linear dimension reduction techniques to map higher dimensional ...
Ming Cui, Jiuxiang Hu, Anshuman Razdan, Peter Wonk...
CIDM
2007
IEEE
15 years 10 months ago
Scalable Clustering for Large High-Dimensional Data Based on Data Summarization
Clustering large data sets with high dimensionality is a challenging data-mining task. This paper presents a framework to perform such a task efficiently. It is based on the notio...
Ying Lai, Ratko Orlandic, Wai Gen Yee, Sachin Kulk...
117
Voted
GLVLSI
2006
IEEE
142views VLSI» more  GLVLSI 2006»
15 years 10 months ago
Dynamic instruction schedulers in a 3-dimensional integration technology
We present the design of high-performance and energy-efficient dynamic instruction schedulers in a 3-Dimensional integration technology. Based on a previous observation that the c...
Kiran Puttaswamy, Gabriel H. Loh
ISVLSI
2003
IEEE
91views VLSI» more  ISVLSI 2003»
15 years 9 months ago
Three-Dimensional Integrated Circuits: Performance, Design Methodology, and CAD Tools
Three-dimensional integration technologies have been proposed in order to mitigate design challenges posed by deep-submicron interconnect. By providing multiple layers of active d...
Shamik Das, Anantha Chandrakasan, Rafael Reif