Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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20 Jul 2008
TL;DR: A new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization is proposed, which aims to create a compressed summary while retaining the main characteristics of the original set of documents.
Abstract: Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent to normalized spectral clustering, is used to group sentences into clusters. Finally, the most informative sentences are selected from each group to form the summary. Experimental results on DUC2005 and DUC2006 data sets demonstrate the improvement of our proposed framework over the implemented existing summarization systems. A further study on the factors that benefit the high performance is also conducted.
304 citations
Cites background from "Pattern Recognition and Machine Lea..."
..., document-term matrix, sentenceterm matrix) and they are not suitable for clustering pairwise similarity matrix [4]....
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TL;DR: A variety of algorithms that have been proposed to address the cluster ensemble problem are described, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches.
Abstract: Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 305–315 DOI: 10.1002/widm.32
304 citations
TL;DR: In recent years, micro-machined electromechanical system inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost.
Abstract: In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.
304 citations
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...This is illustrated in Figure 13 in terms of a probabilistic graphical model [9]....
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20 Oct 2008
TL;DR: GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data, is presented and validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data.
Abstract: This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data.
The segmentation problem is cast as approximate energy minimization in a conditional random field. A new, parallel filtering operator built upon efficient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. An economical search algorithm finds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings.
Advantages include: i) computational efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. Comparison with max-flow indicates up to 60 times greater computational efficiency as well as greater memory efficiency.
GeoS is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. Numerous results on interactive andautomatic segmentation of photographs, video and volumetric medical image data are presented.
302 citations
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...Associated uncertainties have been estimated by assuming Gaussian noise on the measurements [27]....
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TL;DR: A machine-learning-based model for predicting FFR is presented as an alternative to physics-based approaches, and average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR.
Abstract: Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.
300 citations
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..., FFR, is represented by a model built from a database of samples with known characteristics and outcome (7)....
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