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Benyu Zhang
Researcher at Microsoft
Publications - 132
Citations - 7912
Benyu Zhang is an academic researcher from Microsoft. The author has contributed to research in topics: Web page & Web query classification. The author has an hindex of 40, co-authored 132 publications receiving 7832 citations.
Papers
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Journal ArticleDOI
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
TL;DR: A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.
Proceedings ArticleDOI
Graph embedding: a general framework for dimensionality reduction
TL;DR: A new supervised algorithm, Marginal Fisher Analysis (MFA), is proposed, for dimensionality reduction by designing two graphs that characterize the intra-class compactness and inter-class separability, respectively.
Proceedings ArticleDOI
Improving web search results using affinity graph
TL;DR: A novel ranking scheme named Affinity Ranking (AR) is proposed to re-rank search results by optimizing two metrics: diversity -- which indicates the variance of topics in a group of documents; and information richness -- which measures the coverage of a single document to its topic.
Patent
Query-based snippet clustering for search result grouping
TL;DR: In this paper, a clustering architecture that dynamically groups the search result documents into clusters labeled by phrases extracted from search result snippets is proposed. But it is not suitable for the task of semantic segmentation.
Proceedings ArticleDOI
Web-page classification through summarization
TL;DR: This paper gives empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web- page classification algorithms and proposes a new Web summarization-based classification algorithm that achieves an approximately 8.8% improvement over pure-text based methods.