137 search results - page 26 / 28 » Ordinal Embedding: Approximation Algorithms and Dimensionali... |
ICMLA
2007
13 years 9 months ago
2007
Optimal Component Analysis (OCA) is a linear method for feature extraction and dimension reduction. It has been widely used in many applications such as face and object recognitio...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like ...
CORR
2010
Springer
13 years 6 months ago
2010
Springer
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it ...
GIS
2003
ACM
14 years 8 months ago
2003
ACM
With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensi...
COLT
2010
Springer
13 years 5 months ago
2010
Springer
In recent years analysis of complexity of learning Gaussian mixture models from sampled data has received significant attention in computational machine learning and theory commun...