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ICCV
2009
IEEE
16 years 11 months ago
Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represen...
Samuel Gerber, Tolga Tasdizen, Ross Whitaker
VLDB
2004
ACM
178views Database» more  VLDB 2004»
15 years 11 months ago
High-Dimensional OLAP: A Minimal Cubing Approach
Data cube has been playing an essential role in fast OLAP (online analytical processing) in many multi-dimensional data warehouses. However, there exist data sets in applications ...
Xiaolei Li, Jiawei Han, Hector Gonzalez
SDM
2009
SIAM
205views Data Mining» more  SDM 2009»
16 years 3 months ago
Identifying Information-Rich Subspace Trends in High-Dimensional Data.
Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in m...
Chandan K. Reddy, Snehal Pokharkar
ICML
2004
IEEE
15 years 11 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
VISSYM
2007
15 years 8 months ago
Dimensional Congruence for Interactive Visual Data Mining and Knowledge Discovery
Many authors in the field of 3D human computer interaction have described the advantages of 3D user interfaces: Intuitive metaphors from daily life, immersive workspaces, virtual ...
Sebastian Baumgärtner, Achim Ebert, Matthias ...