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ICML
2004
IEEE
16 years 14 days ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
216
Voted
IMC
2004
ACM
16 years 14 days ago
Modeling distances in large-scale networks by matrix factorization
In this paper, we propose a model for representing and predicting distances in large-scale networks by matrix factorization. The model is useful for network distance sensitive app...
Yun Mao, Lawrence K. Saul
MM
2004
ACM
122views Multimedia» more  MM 2004»
16 years 14 days ago
Context for semantic metadata
This article argues for the growing importance of quality metadata and the equation of that quality with precision and semantic grounding. Such semantic grounding requires metadat...
Kenneth B. Haase
SIGIR
2004
ACM
16 years 14 days ago
Focused named entity recognition using machine learning
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that th...
Li Zhang, Yue Pan, Tong Zhang
CIKM
2004
Springer
16 years 13 days ago
Scalable sequential pattern mining for biological sequences
Biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., “don’t care”) of arbitrary size. Mining frequent patterns in such sequences ...
Ke Wang, Yabo Xu, Jeffrey Xu Yu