148 search results - page 4 / 30 » DiscLDA: Discriminative Learning for Dimensionality Reductio... |
ICML
2007
IEEE
14 years 7 months ago
2007
IEEE
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
ICML
2007
IEEE
14 years 7 months ago
2007
IEEE
In many real-world applications, Euclidean distance in the original space is not good due to the curse of dimensionality. In this paper, we propose a new method, called Discrimina...
IEEEMM
2007
13 years 6 months ago
2007
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...
PAMI
2008
13 years 5 months ago
2008
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
ICPR
2008
IEEE
14 years 20 days ago
2008
IEEE
In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neig...