8 Result(s)
within
Hongyuan Zha
Database Management
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Chapter and Conference Paper
Sequential Multi-fusion Network for Multi-channel Video CTR Prediction
In this work, we study video click-through rate (CTR) prediction, crucial for the refinement of video recommendation and the revenue of video advertising. Existing studies have verified the importance of model...
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Chapter and Conference Paper
Personalized Prescription for Comorbidity
Personalized medicine (PM) aiming at tailoring medical treatment to individual patient is critical in guiding precision prescription. An important challenge for PM is comorbidity due to the complex interrelati...
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Chapter
Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond
The bag-of-words representation commonly used in text analysis can be analyzed very efficiently and retains a great deal of useful information, but it is also troublesome because the same thought can be expres...
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Chapter and Conference Paper
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both t...
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Chapter and Conference Paper
IKNN: Informative K-Nearest Neighbor Pattern Classification
The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tun...
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Chapter and Conference Paper
Extracting Shared Topics of Multiple Documents
In this paper, we present a weighted graph based method to simultaneously compare the textual content of two or more documents and extract the shared (sub)topics of them, if available. A set of documents are m...
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Chapter and Conference Paper
Nonlinear Dimension Reduction via Local Tangent Space Alignment
In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry...
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Chapter and Conference Paper
Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Selfaggregation i...
