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Chi-Hung Chi

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3 papers

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IJCAI Conference 2018 Conference Paper

A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter

Yunwei Zhao
Can Wang
Chi-Hung Chi
Kwok-Yan Lam
Sen Wang

The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade. Experiments on Twitter data show that the semantic model outperforms the transactional model, if the exterior pattern is less directly observable (i. e. hashtag cascade). When it becomes more directly observable (i. e. retweet and url cascades), the semantic method yet delivers approximate accuracy (i. e. url cascade) or even worse accuracy (i. e. retweet cascade). Further, we demonstrate that the transactional and semantic models are not independent, and the performance gets greatly enhanced when combining both.

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AAAI Conference 2015 Conference Paper

Coupled Interdependent Attribute Analysis on Mixed Data

Can Wang
Chi-Hung Chi
Wei Zhou
Raymond Wong

In the real-world applications, heterogeneous interdependent attributes that consist of both discrete and numerical variables can be observed ubiquitously. The usual representation of these data sets is an information table, assuming the independence of attributes. However, very often, they are actually interdependent on one another, either explicitly or implicitly. Limited research has been conducted in analyzing such attribute interactions, which causes the analysis results to be more local than global. This paper proposes the coupled heterogeneous attribute analysis to capture the interdependence among mixed data by addressing coupling context and coupling weights in unsupervised learning. Such global couplings integrate the interactions within discrete attributes, within numerical attributes and across them to form the coupled representation for mixed-type objects based on dimension conversion and feature selection. This work makes one step forward towards explicitly modeling the interdependence of heterogeneous attributes among mixed data, verified by the applications in data structure analysis, data clustering evaluation, and density comparison. Substantial experiments on 12 UCI data sets show that our approach can effectively capture the global couplings of heterogeneous attributes and outperforms the state-of-the-art methods, supported by statistical analysis.

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AAAI Conference 2010 Conference Paper

Non-I.I.D. Multi-Instance Dimensionality Reduction by Learning a Maximum Bag Margin Subspace

Wei Ping
Ye Xu
Kexin Ren
Chi-Hung Chi
Furao Shen

Multi-instance learning, as other machine learning tasks, also suffers from the curse of dimensionality. Although dimensionality reduction methods have been investigated for many years, multi-instance dimensionality reduction methods remain untouched. On the other hand, most algorithms in multi-instance framework treat instances in each bag as independently and identically distributed (i. i. d.) samples, which fail to utilize the structure information conveyed by instances in a bag. In this paper, we propose a multi-instance dimensionality reduction method, which treats instances in each bag as non-i. i. d. samples. To capture the structure information conveyed by instances in a bag, we regard every bag as a whole entity. To utilize the bag label information, we maximize the bag margin between positive and negative bags. By maximizing the defined bag margin objective function, we learn a subspace to obtain salient representation of original data. Experiments demonstrate the effectiveness of the method.

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