Nemirovsky, "On sparse representation in pairs of bases," IEEE Trans. Inf. Theory, vol. 49, no. 6, pp. 1579-1581, Jun. 2003.A. Feuer and A. Nemirovski, "On sparse representation in pairs of bases," IEEE Trans. Inform. Theory, vol. 49, no. 6, pp. 1579-1581, 2003....
On sparse representation in pairs of bases 来自 IEEEXplore 喜欢 0 阅读量: 83 作者: A Feuer 摘要: optimization by linear programming minimization when searching for the unique sparse representation. We establish here that the EB condition is both sufficient and necessary. 关键词: linear ...
In this study, the authors investigated some new inequalities on sparse representation for pairs of bases and frames, which would enrich the theory ensemble. First, for fixed pairs of bases, frames and the signal to be represented, we presented the bounds (which can be used in practice directl...
Consequently, when K <; 1/μ(D), the proposed algorithm solves the unique sparse representation problem for this structured dictionary in polynomial time. We further show that the same method can be extended to many other pairs of bases, one of which must have local atoms. Examples include ...
Modeling Relation Paths for Representation Learning of Knowledge Bases Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu EMNLP 2015 Embedding Entities and Relations for Learning and Inference in Knowledge Bases Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng ...
We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techniques from several inductive logic programming systems. ...
(q1,q2) pairs as shown in Fig.3. When a term is assigned two or more qualifiers (e.g.,t2/Z/Ufor Paper 3 - Fig.3), this means that a paper deals with a facet of a characteristic of the considered topic. In such a situation, we consider it as though the qualifiers were ...
摘要:Efficient coding of speech and audio in a distributed system requires that quantization errors across nodes are uncorrelated. Yet, with conventional methods at low bitrates, quantization levels become increasingly sparse, which does not correspond to the distribution of the ...
RESCAL is a tensor factorization approach to knowledge representation learning, which is able to perform collective learning via the latent components of the factorization. SE: Learning Structured Embeddings of Knowledge Bases.Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio.AAAI 2011.paper ...
(1) Supervised learning, i.e., learning or setting up models from training datasets (pairs of input/output data or labelled data) for classification and regression. The essential problem in supervised learning is to select relevant models based on experimental data that is accurate, generalizable ...