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Trans2Vec: Learning transaction embedding via items and frequent itemsets 22nd

Trans2Vec: Learning transaction embedding via items and freq...

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22nd Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2018

作者： Nguyen, Dang Nguyen, Tu Dinh Luo, Wei Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9783319930398

Learning meaningful and effective representations for transaction data is a crucial prerequisite for transaction classification and clustering tasks. Traditional methods which use frequent itemsets (FIs) as features often suffer from the data sparsity and high-dimensionality problems. Several supervised methods based on discriminative FIs have been proposed to address these disadvantages, but they require transaction labels, thus rendering them inapplicable to real-world applications where labels are not given. In this paper, we propose an unsupervised method which learns low-dimensional continuous vectors for transactions based on information of both singleton items and FIs. We demonstrate the superior performance of our proposed method in classifying transactions on four datasets compared with several state-of-the-art baselines. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Classification (of information)

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Keeping up with innovation: A predictive framework for modeling healthcare data with evolving clinical interventions 14

Keeping up with innovation: A predictive framework for model...

引用

14th SIAM International Conference on data Mining, SDM 2014

作者： Gupta, Sunil Kumar Rana, Santu Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Geelong Waura Ponds Campus Victoria Australia

ISBN: (纸本)9781510811515

Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture variations. Interventions also evolve over time as more advanced interventions replace older ones. To this end, we propose a Bayesian nonparametric, supervised framework that models a set of intervention groups through a mixture distribution building a separate prediction rule for each group, and allows the mixture distribution to change with time. This is achieved by using a hierarchical Dirichlet process mixture model over the interventions. The outcome is then modeled as conditional on both the latent grouping and the disease information through a Bayesian logistic regression. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines. Copyright © SIAM.

关键词： data mining

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Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine 1

引用

17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013

作者： Nguyen, Tu Dinh Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong VIC Australia

ISBN: (数字)9783642374531

ISBN: (纸本)9783642374524

Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann machine (***), as it seamlessly integrates multiple data types for each patient aggregated over time and outputs a homogeneous representation called "latent profile" that can be used for patient clustering, visualisation, disease correlation analysis and prediction. We demonstrate that the method outperforms all baselines on these tasks - the primary characteristics of patients in the same groups are able to be identified and the good result can be achieved for the diagnosis codes prediction. © Springer-Verlag 2013.

关键词： Diagnosis

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What shall i share and with Whom? - A multi-task learning formulation using multi-faceted task relationships

What shall i share and with Whom? - A multi-task learning fo...

引用

SIAM International Conference on data Mining 2015, SDM 2015

作者： Gupta, Sunil Rana, Santu Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Geelong Waurn Ponds Campus VIC Australia

ISBN: (纸本)9781510811522

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?. Copyright © SIAM.

关键词： Learning systems

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Geometric enclosing networks 27

Geometric enclosing networks

引用

27th International Joint Conference on Artificial Intelligence, IJCAI 2018

作者： Le, Trung Vu, Hung Nguyen, Tu Dinh Phung, Dinh Faculty of Information Technology Monash University Australia Center for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9780999241127

Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optimization approach to address this problem. Orthogonal to current state-of-the-art density-based approaches, most notably VAE and GAN, we present a fresh new idea that borrows the principle of minimal enclosing ball to train a generator G (z) in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere. We develop theory to guarantee that the mapping is bijective so that its inverse from feature space to data space results in expressive nonlinear contours to describe the data manifold, hence ensuring data generated are also lying on the data manifold learned from training data. Our model enjoys a nice geometric interpretation, hence termed Geometric Enclosing Networks (GEN), and possesses some key advantages over its rivals, namely simple and easy-to-control optimization formulation, avoidance of mode collapsing and efficiently learn data manifold representation in a completely unsupervised manner. We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.

关键词： Geometry

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A random finite set model for data clustering 17

A random finite set model for data clustering

引用

17th International Conference on Information Fusion, FUSION 2014

作者： Phung, Dinh Vo, Ba-Ngu Center for Pattern Recognition and Data Analytics Deakin University Australia Department of Electrical and Computer Engineering Curtin University Australia

ISBN: (纸本)9788490123553

The goal of data clustering is to partition data points into groups to optimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clustering methods require the user to specify the number of clusters, which is not available in advance. This paper proposes a new class of models for data clustering that addresses set-valued data as well as unknown number of clusters, using a Dirichlet Process mixture of Poisson random finite sets. We also develop an efficient Markov Chain Monte Carlo posterior inference technique that can learn the number of clusters and mixture parameters automatically from the data. Numerical studies are presented to demonstrate the salient features of this new model, in particular its capacity to discover extremely unbalanced clusters in data. © 2014 International Society of Information Fusion.

关键词： Mixtures

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Batch Normalized Deep Boltzmann Machines 10

Batch Normalized Deep Boltzmann Machines

引用

10th Asian Conference on Machine Learning, ACML 2018

作者： Vu, Hung Nguyen, Tu Dinh Le, Trung Luo, Wei Phung, Dinh Center for Pattern Recognition and Data Analytics Deakin University Geelong Australia Monash University ClaytonVIC3800 Australia

Training Deep Boltzmann Machines (DBMs) is a challenging task in deep generative model studies. The careless training usually leads to a divergence or a useless model. We discover that this phenomenon is due to the change of DBM layers’ input signals during model parameter updates, similar to other deterministic deep networks such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). The change of layers’ input distributions not only complicates the learning process but also causes redundant neurons that simply imitate the others’ behaviors. Although this phenomenon can be coped using batch normalization in deep learning, integrating this technique into the probabilistic network of DBMs is a challenging problem since it has to satisfy two conditions of energy function and conditional probabilities. In this paper, we introduce Batch Normalized Deep Boltzmann Machines (BNDBMs) that meet both aforementioned conditions and successfully combine batch normalization and DBMs into the same framework. However, unlike CNNs, due to the probabilistic nature of DBMs, training DBMs with batch normalization has some differences: i) fixing shift parameters β but learning scale parameters γ;ii) avoiding normalizing the first hidden layer and iii) maintaining multiple pairs of population means and variances per neuron rather than one pair in CNNs. We observe that our proposed BNDBMs can stabilize the input signals of network layers and facilitate the training process as well as improve the model quality. More interestingly, BNDBMs can be trained successfully without pretraining, which is usually a mandatory step in most existing DBMs. The experimental results in MNIST, Fashion-MNIST and Caltech 101 Silhouette datasets show that our BNDBMs outperform DBMs and centered DBMs in terms of feature representation and classification accuracy (3.98% and 5.84% average improvement for pretraining and no pretraining respectively). © 2018 H. Vu, T.D. Nguyen, T. Le, W. Luo &

关键词： Network layers

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Large-scale online kernel learning with random feature reparameterization 26

Large-scale online kernel learning with random feature repar...

引用

26th International Joint Conference on Artificial Intelligence, IJCAI 2017

作者： Nguyen, Tu Dinh Le, Trung Bui, Hung Phung, Dinh Adobe Research Adobe Systems Inc. Australia Center for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9780999241103

A typical online kernel learning method faces two fundamental issues: the complexity in dealing with a huge number of observed data points (a.k.a the curse of kernelization) and the difficulty in learning kernel parameters, which often assumed to be fixed. Random Fourier feature is a recent and effective approach to address the former by approximating the shift-invariant kernel function via Bocher's theorem, and allows the model to be maintained directly in the random feature space with a fixed dimension, hence the model size remains constant w.r.t. data size. We further introduce in this paper the reparameterized random feature (RRF), a random feature framework for large-scale online kernel learning to address both aforementioned challenges. Our initial intuition comes from the so-called 'reparameterization trick' [Kingma and Welling, 2014] to lift the source of randomness of Fourier components to another space which can be independently sampled, so that stochastic gradient of the kernel parameters can be analytically derived. We develop a well-founded underlying theory for our method, including a general way to reparameterize the kernel, and a new tighter error bound on the approximation quality. This view further inspires a direct application of stochastic gradient descent for updating our model under an online learning setting. We then conducted extensive experiments on several large-scale datasets where we demonstrate that our work achieves state-of-the-art performance in both learning efficacy and efficiency.

关键词： Stochastic systems

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Robust anomaly detection in videos using multilevel representations 33

Robust anomaly detection in videos using multilevel represen...

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33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

作者： Vu, Hung Nguyen, Tu Dinh Le, Trung Luo, Wei Phung, Dinh Center for Pattern Recognition and Data Analytics Deakin University Geelong Australia Monash University ClaytonVIC3800 Australia

ISBN: (纸本)9781577358091

Detecting anomalies in surveillance videos has long been an important but unsolved problem. In particular, many existing solutions are overly sensitive to (often ephemeral) visual artifacts in the raw video data, resulting in false positives and fragmented detection regions. To overcome such sensitivity and to capture true anomalies with semantic significance, one natural idea is to seek validation from abstract representations of the videos. This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data. The framework consists of three main components: 1) representation learning using Denoising Autoencoders, 2) level-wise representation generation using Conditional Generative Adversarial Networks, and 3) consolidating anomalous regions detected at each representation level. Our proposed multilevel detector shows a significant improvement in pixel-level Equal Error Rate, namely 11.35%, 12.32% and 4.31% improvement in UCSD Ped 1, UCSD Ped 2 and Avenue datasets respectively. In addition, the model allowed us to detect mislabeled anomalies in the UCDS Ped 1. Copyright © 2019, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Anomaly detection

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Cascade Bayesian optimization 29th

引用

29th Australasian Joint Conference on Artificial Intelligence, AI 2016

作者： Nguyen, Thanh Dai Gupta, Sunil Rana, Santu Nguyen, Vu Venkatesh, Svetha Deane, Kyle J. Sanders, Paul G. Center for Pattern Recognition and Data Analytics Deakin University Geelong3216 Australia Materials Science and Engineering Michigan Technological University Houghton United States

ISBN: (纸本)9783319501260

Multi-stage cascade processes are fairly common, especially in manufacturing industry. Precursors or raw materials are transformed at each stage before being used as the input to the next stage. Setting the right control parameters at each stage is important to achieve high quality products at low cost. Finding the right parameters via trial and error approach can be time consuming. Bayesian optimization is an efficient way to optimize costly black-box function. We extend the standard Bayesian optimization approach to the cascade process through formulating a series of optimization problems that are solved sequentially from the final stage to the first stage. Epistemic uncertainties are effectively utilized in the formulation. Further, cost of the parameters are also included to find cost-efficient solutions. Experiments performed on a simulated testbed of Al-Sc heat treatment through a three-stage process showed considerable efficiency gain over a naïve optimization approach. © Springer International Publishing AG 2016.

关键词： Uncertainty analysis

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