1148 search results - page 82 / 230 » Dimensionality Reduction with Image Data |
138
Voted
JCP
2007
15 years 3 months ago
2007
Abstract— Analyzing unknown data sets such as multispectral images often requires unsupervised techniques. Data clustering is a well known and widely used approach in such cases....
114
Voted
COMPGEOM
2009
ACM
15 years 10 months ago
2009
ACM
Nonlinear dimensionality reduction (NLDR) algorithms such as Isomap, LLE and Laplacian Eigenmaps address the problem of representing high-dimensional nonlinear data in terms of lo...
167
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PSIVT
2009
Springer
15 years 10 months ago
2009
Springer
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
154
Voted
AAAI
2012
13 years 5 months ago
2012
Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretabi...
142
Voted
SIGMOD
2002
ACM
16 years 3 months ago
2002
ACM
Applications like multimedia retrieval require efficient support for similarity search on large data collections. Yet, nearest neighbor search is a difficult problem in high dimen...