检索结果-内蒙古大学图书馆

Dirichlet Process Mixture Models with Pairwise Constraints for data Clustering

Annals of data Science 2016年第2期3卷 205-223页

作者： Li, Cheng Rana, Santu Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

The Dirichlet process mixture (DPM) model, a typical Bayesian nonparametric model, can infer the number of clusters automatically, and thus performing priority in data clustering. This paper investigates the influence of pairwise constraints in the DPM model. The pairwise constraint, known as two types: must-link (ML) and cannot-link (CL) constraints, indicates the relationship between two data points. We have proposed two relevant models which incorporate pairwise constraints: the constrained DPM (C-DPM) and the constrained DPM with selected constraints (SC-DPM). In C-DPM, the concept of chunklet is introduced. ML constraints are compiled into chunklets and CL constraints exist between chunklets. We derive the Gibbs sampling of the C-DPM based on chunklets. We further propose a principled approach to select the most useful constraints, which will be incorporated into the SC-DPM. We evaluate the proposed models based on three real datasets: 20 Newsgroups dataset, NUS-WIDE image dataset and Facebook comments datasets we collected by ourselves. Our SC-DPM performs priority in data clustering. In addition, our SC-DPM can be potentially used for short-text clustering. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Mixtures

来源：评论

学校读者我要写书评

暂无评论

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

引用

International Journal of data Science and analytics 2017年第3期4卷 209-231页

作者： Dao, Bo Nguyen, Thin Venkatesh, Svetha Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC3216 Australia

Social media are an online means of interaction among individuals. People are increasingly using social media, especially online communities, to discuss health concerns and seek support. Understanding topics, sentiment, and structures of these communities informs important aspects of health-related conditions. There has been growing research interest in analysing online mental health communities;however, analysis of these communities with health concerns has been limited. This paper investigates and identifies latent meta-groups of online communities with and without mental health-related conditions including depression and autism. Large datasets from online communities were crawled. We analyse sentiment-based, psycholinguistics-based and topic-based features from blog posts made by members of these online communities. The work focuses on using nonparametric methods to infer latent topics automatically from the corpus of affective words in the blog posts. The visualization of the discovered meta-communities in their use of latent topics shows a difference between the groups. This presents evidence of the emotion-bearing difference in online mental health-related communities, suggesting a possible angle for support and intervention. The methodology might offer potential machine learning techniques for research and practice in psychiatry. © 2017, Springer International Publishing AG.

关键词： Health

来源：评论

学校读者我要写书评

暂无评论

Dual space gradient descent for online learning 30

Dual space gradient descent for online learning

引用

30th Annual Conference on Neural Information Processing Systems, NIPS 2016

作者： Le, Trung Nguyen, Tu Dinh Nguyen, Vu Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University Australia

One crucial goal in kernel online learning is to bound the model size. Common approaches employ budget maintenance procedures to restrict the model sizes using removal, projection, or merging strategies. Although projection and merging, in the literature, are known to be the most effective strategies, they demand extensive computation whilst removal strategy fails to retain information of the removed vectors. An alternative way to address the model size problem is to apply random features to approximate the kernel function. This allows the model to be maintained directly in the random feature space, hence effectively resolve the curse of kernelization. However, this approach still suffers from a serious shortcoming as it needs to use a high dimensional random feature space to achieve a sufficiently accurate kernel approximation. Consequently, it leads to a significant increase in the computational cost. To address all of these aforementioned challenges, we present in this paper the Dual Space Gradient Descent (DualSGD), a novel framework that utilizes random features as an auxiliary space to maintain information from data points removed during budget maintenance. Consequently, our approach permits the budget to be maintained in a simple, direct and elegant way while simultaneously mitigating the impact of the dimensionality issue on learning performance. We further provide convergence analysis and extensively conduct experiments on five real-world datasets to demonstrate the predictive performance and scalability of our proposed method in comparison with the state-of-the-art baselines. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Merging

来源：评论

学校读者我要写书评

暂无评论

Streaming variational inference for dirichlet process mixtures 7

Streaming variational inference for dirichlet process mixtur...

引用

7th Asian Conference on Machine Learning, ACML 2015

作者： Huynh, Viet Phung, Dinh Venkatesh, Svetha Pattern Recognition and Data Analytics Centre Deakin University Australia

Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they require truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our experiments demonstrate the usefulness of our framework in both synthetic and real-world data. © 2015 V. Huynh, D. Phung & S. Venkatesh.

关键词： Mixtures

来源：评论

学校读者我要写书评

暂无评论

Unsupervised inference of significant locations from WiFi data for understanding human dynamics 14

Unsupervised inference of significant locations from WiFi da...

引用

13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014

作者： Nguyen, Thanh-Binh Nguyen, Thuong Luo, Wei Venkatesh, Svetha Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781450334013

Motion and location activities are essential to understanding human dynamics. This paper presents a method for discovering significant locations and individuals' daily routines from WiFi data, a data source considered more suitable for analyzing human dynamics than GPS data. Our method determines significant locations by clustering access points in close proximity using the Affinity Propagation algorithm. We demonstrate the method on the MDC dataset that includes more than 30 million WiFi scans. The experimental results show a high clustering performance for most of the users. The discovered location trajectories revealed interesting mobility patterns of mobile phone users. The human dynamics of participants is reflected through the entropy of the location distributions which shows interesting correlation with the age and occupations of users. Quantitative results are presented to support our proposed approach. Copyright © 2014 ACM 978-1-4503-3304-7/14/11 $15.00.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

Stable clinical prediction using graph support vector machines 23

Stable clinical prediction using graph support vector machin...

引用

23rd International Conference on pattern recognition, ICPR 2016

作者： Kamkar, Iman Gupta, Sunil Li, Cheng Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509048472

The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard similarity among features. Our method employs a convex function to penalize the pairwise l∞-norm of connected feature coefficients in the graph. We apply the alternating direction method of multipliers to solve the proposed formulation. Our experiments are conducted on a synthetic and three real-world hospital datasets. We show that our proposed method is more stable than the state-of-the-art feature selection and classification techniques in terms of three stability measures namely, Jaccard similarity measure, Spearman's rank correlation coefficient and Kuncheva index. We further show that our method has resulted in better classification performance compared to the baselines. © 2016 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Nonparametric discovery of online mental health-related communities

Nonparametric discovery of online mental health-related comm...

引用

IEEE International Conference on data Science and Advanced analytics, DSAA 2015

作者： Dao, Bo Nguyen, Thin Venkatesh, Svetha Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467382731

People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add knowledge to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing online mental health communities;however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online Live Journal communities with and without mental health-related conditions including depression and autism. Latent topics for mood tags, affective words, and generic words in the content of the posts made in these communities were learned using nonparametric topic modelling. These representations were then input into a nonparametric clustering to discover meta-groups among the communities. The best performance results can be achieved on clustering communities with latent mood-based representation for such communities. The study also found significant differences in usage latent topics for mood tags and affective features between online communities with and without affective disorders. The findings reveal useful insights into hyper-group detection of online mental health-related communities. © 2015 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A nonparametric Bayesian Poisson gamma model for count data

A nonparametric Bayesian Poisson gamma model for count data

引用

21st International Conference on pattern recognition, ICPR 2012

作者： Gupta, Sunil Kumar Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9784990644109

We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models. © 2012 ICPR Org Committee.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

Extraction of latent patterns and contexts from social hones...

引用

11th IEEE International Conference on Pervasive Computing and Communications, PerCom 2013

作者： Nguyen, Thuong Phung, Dinh Gupta, Sunil Venkatesh, S. Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467345750

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data. © 2013 IEEE.

关键词： Ubiquitous computing

来源：评论

学校读者我要写书评

暂无评论

Exploiting feature relationships towards stable feature selection

Exploiting feature relationships towards stable feature sele...

引用

IEEE International Conference on data Science and Advanced analytics, DSAA 2015

作者： Kamkar, Iman Gupta, Sunil Kumar Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467382731

Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods. © 2015 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：