206 search results - page 6 / 42 » PCA in Autocorrelation Space |
NIPS
2008
13 years 9 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
FOCS
2010
IEEE
13 years 5 months ago
2010
IEEE
We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded ...
BMVC
2000
13 years 9 months ago
2000
High-dimensional data, such as images represented as points in the space spanned by their pixel values, can often be described in a significantly smaller number of dimensions than...
IDEAL
2003
Springer
14 years 23 days ago
2003
Springer
To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with Fuzzy clustering. The prop...
IJISTA
2007
13 years 7 months ago
2007
: This paper presents an online learning framework for the behavior of an articulated body by capturing its motion using real-time video. In our proposed framework, supervised lear...