Author name cluster

Nathan Liu

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5 papers

1 author row

AIIM Journal 2021 Journal Article

Practical fine-grained learning based anomaly classification for ECG image

Qing Cao
Nan Du
Li Yu
Ming Zuo
Jingsheng Lin
Nathan Liu
Erheng Zhong
Zizhu Liu

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for assessing heart function and diagnosing cardiovascular diseases. Automated anomaly detection for ECG plays an important role in improving patient diagnosis efficiency and reducing healthcare costs. Practically, due to the limits of electronics support or the medical system setting, image is a more common format for large-scale ECG storage in most clinical institutions. To guarantee an automated ECG detection model's scalability and practicality in clinical applications, taking good advantage of ECG images is crucial. However, existing time digital-based discriminative models fail to learn from images effectively for two reasons. First of all, the signals recorded on images have much lower resolution and higher noise, which makes it impractical to extract precise ECG signals following existing techniques. Meanwhile, the differences between abnormal signals are usually subtle, and they may be overwhelmed by the noises in the images as well. Towards this end, we design a novel neural framework that can be directly applied to massive ECG images determining various types of cardiology abnormalities. It classifies fine-grained ECG images based on weakly supervised strategy, in which case only image-level labeling is required. By eliminating the need for part annotations, the proposed method can result in significant savings in annotation time and cost. The effectiveness of the method is demonstrated by experimental results on two real ECG datasets.

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AAAI Conference 2012 Conference Paper

Discovering Spammers in Social Networks

Yin Zhu
Xiao Wang
Erheng Zhong
Nathan Liu
He Li
Qiang Yang

As the popularity of the social media increases, as evidenced in Twitter, Facebook and China’s Renren, spamming activities also picked up in numbers and variety. On social network sites, spammers often disguise themselves by creating fake accounts and hijacking normal users’ accounts for personal gains. Different from the spammers in traditional systems such as SMS and email, spammers in social media behave like normal users and they continue to change their spamming strategies to fool anti-spamming systems. However, due to the privacy and resource concerns, many social media websites cannot fully monitor all the contents of users, making many of the previous approaches, such as topology-based and content-classiﬁcation-based methods, infeasible to use. In this paper, we propose a Supervised Matrix Factorization method with Social Regularization (SMFSR) for spammer detection in social networks that exploits both social activities as well as users’ social relations in an innovative and highly scalable manner. The proposed method detects spammers collectively based on users’ social actions and social relations. We have empirically tested our method on data from Renren. com, which is one of the largest social networks in China, and demonstrated that our new method can improve the detection performance signiﬁcantly.

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AAAI Conference 2011 Conference Paper

Active Dual Collaborative Filtering with Both Item and Attribute Feedback

Luheng He
Nathan Liu
Qiang Yang

The new user problem (aka user cold start) is very common in online recommender systems. Active collaborative ﬁltering (active CF) tries to solve this problem by intelligently soliciting user feedback in order to build an initial user proﬁle with minimal costs. Existing methods only query the user for feedback on items, while users can have preferences over items as well as certain item attributes. In this paper, we extend active CF via user feedback on both items and attributes. For example, when making movie recommendations, the system can ask users for not only their favorite movies, but also attributes such as genres, actors, etc. We design a uniﬁed active CF framework for incorporating both item and attribute feedback based on the random walk model. We test the active CF algorithm on realworld movie recommendation data sets to demonstrate that appropriately querying for both item and feature feedback can signiﬁcantly reduce the overall user effort measured in terms of number of queries. We show that we can achieve much better recommendation quality as compared to traditional active CF methods that support only item feedback.

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IJCAI Conference 2011 Conference Paper

Incorporating Reviewer and Product Information for Review Rating Prediction

Fangtao Li
Nathan Liu
Hongwei Jin
Kai Zhao
Qiang Yang
Xiaoyan Zhu

Among sentiment analysis tasks, review rating prediction is more helpful than binary (positive and negative) classification, especially when the consumers want to compare two good products. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text feature are modeled as a three-dimension tensor. The tensor factorization technique is employed to reduce the sparsity and complexity problems. The experiment results demonstrate the effectiveness of our model. We achieve significant improvement as compared with the state of the art methods, especially for the reviews with unpopular products and inactive reviewers.

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AAAI Conference 2010 Conference Paper

Transfer Learning in Collaborative Filtering for Sparsity Reduction

Weike Pan
Evan Xiang
Nathan Liu
Qiang Yang

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem.

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