In this paper, an unsupervised learning algorithm, neighborhood linear embedding (NLE), is proposed to discover the intrinsic structures such as neighborhood relationships, global ...
Shuzhi Sam Ge, Feng Guan, Yaozhang Pan, Ai Poh Loh
We consider embeddings of structures which preserve spectra: if g : M → S with S computable, then M should have the same Turing degree spectrum (as a structure) that g(M) has (a...
This paper presents a fully secure functional encryption scheme for a wide class of relations, that are specified by non-monotone access structures combined with inner-product rel...
An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability 1 to the optimal path ...
This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For e...