IEEE journal of biomedical and health informatics, Jan 18, 2016
Mental illness has a deep impact on individuals, families, and by extension, society as a whole. ... more Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620,000 posts made by 80,000 users in 247 online communities. We have extracted the topics and psycho-linguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modelling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed em...
†Centre for Pattern Recognition and Data Analytics, Deakin University, Australia. ‡School of Medi... more †Centre for Pattern Recognition and Data Analytics, Deakin University, Australia. ‡School of Medicine, Deakin University; Barwon Health, PO Box 291, Geelong, 3220, Australia; Department of Psychiatry, Mental Health Research Institute, The University of Melbourne; Royal Melbourne Hospital Vic 3050; Orygen Youth Health Research Centre. ∗Barwon Health,.PO Box 291, Geelong, 3220. School of Medicine, Deakin University; Barwon Health, PO Box 291, Geelong, 3220, Australia; ... School of Information Technology, Deakin University, ...
Abstract We present a novel method for document clustering using sparse representation of documen... more Abstract We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ell_ {1}-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to ...
Page 1. Preprint manuscript No. (will be inserted by the editor) Anomaly Detection in Large-Scale... more Page 1. Preprint manuscript No. (will be inserted by the editor) Anomaly Detection in Large-Scale Data Stream Networks Duc-Son Pham†· Svetha Venkatesh‡ · Mihai Lazarescu†· Saha Budhaditya‡ Received: date / Accepted: date ...
Abstract We present a novel approach to improving subspace clustering by exploiting the spatial c... more Abstract We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso ...
ABSTRACT Prognosis, such as predicting mortality, is common in medicine. When confronted with sma... more ABSTRACT Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.
Abstract We present a novel method for document clustering using sparse representation of documen... more Abstract We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ell_ {1}-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to ...
Abstract Detecting network traffic volume anomalies in real time is a key problem as it enables m... more Abstract Detecting network traffic volume anomalies in real time is a key problem as it enables measures to be taken to prevent network congestion which severely affects the end users. Several techniques based on principal component analysis (PCA) have been outlined in the past which detect volume anomalies as outliers in the residual subspace. However, these methods are not scalable to networks with a large number of links. We address this scalability issue with a new approach inspired from the recently developed ...
IEEE journal of biomedical and health informatics, Jan 18, 2016
Mental illness has a deep impact on individuals, families, and by extension, society as a whole. ... more Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620,000 posts made by 80,000 users in 247 online communities. We have extracted the topics and psycho-linguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modelling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed em...
†Centre for Pattern Recognition and Data Analytics, Deakin University, Australia. ‡School of Medi... more †Centre for Pattern Recognition and Data Analytics, Deakin University, Australia. ‡School of Medicine, Deakin University; Barwon Health, PO Box 291, Geelong, 3220, Australia; Department of Psychiatry, Mental Health Research Institute, The University of Melbourne; Royal Melbourne Hospital Vic 3050; Orygen Youth Health Research Centre. ∗Barwon Health,.PO Box 291, Geelong, 3220. School of Medicine, Deakin University; Barwon Health, PO Box 291, Geelong, 3220, Australia; ... School of Information Technology, Deakin University, ...
Abstract We present a novel method for document clustering using sparse representation of documen... more Abstract We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ell_ {1}-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to ...
Page 1. Preprint manuscript No. (will be inserted by the editor) Anomaly Detection in Large-Scale... more Page 1. Preprint manuscript No. (will be inserted by the editor) Anomaly Detection in Large-Scale Data Stream Networks Duc-Son Pham†· Svetha Venkatesh‡ · Mihai Lazarescu†· Saha Budhaditya‡ Received: date / Accepted: date ...
Abstract We present a novel approach to improving subspace clustering by exploiting the spatial c... more Abstract We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso ...
ABSTRACT Prognosis, such as predicting mortality, is common in medicine. When confronted with sma... more ABSTRACT Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.
Abstract We present a novel method for document clustering using sparse representation of documen... more Abstract We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ell_ {1}-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to ...
Abstract Detecting network traffic volume anomalies in real time is a key problem as it enables m... more Abstract Detecting network traffic volume anomalies in real time is a key problem as it enables measures to be taken to prevent network congestion which severely affects the end users. Several techniques based on principal component analysis (PCA) have been outlined in the past which detect volume anomalies as outliers in the residual subspace. However, these methods are not scalable to networks with a large number of links. We address this scalability issue with a new approach inspired from the recently developed ...
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