NIPS
2008
13 years 6 months ago
2008
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound...
NIPS
2008
13 years 6 months ago
2008
We study the convergence and the rate of convergence of a local manifold learning algorithm: LTSA [13]. The main technical tool is the perturbation analysis on the linear invarian...
NIPS
2008
13 years 6 months ago
2008
Large networks of spiking neurons show abrupt changes in their collective dynamics resembling phase transitions studied in statistical physics. An example of this phenomenon is th...
NIPS
2008
13 years 6 months ago
2008
In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks: cardinality-constrained rank-one variance maximization and m...
NIPS
2008
13 years 6 months ago
2008
This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for `local ranking', in the sense that the r...
NIPS
2008
13 years 6 months ago
2008
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
NIPS
2008
13 years 6 months ago
2008
We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...
NIPS
2008
13 years 6 months ago
2008
It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from audio, image, and video data. Recent research has be...
NIPS
2008
13 years 6 months ago
2008
NIPS
2008
13 years 6 months ago
2008
This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions. For d covariates, there are 2d basis coefficients to estimate,...