Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Cao, Xiaofeng; Bao-zhi, Qiu; Li, Xiangli; Shi, Zenglin; Xu, Guandong; Xu, Jianliang

doi:10.1109/tnnls.2018.2874458

Cited by 8 publications

(4 citation statements)

References 65 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GBM outperformed all other approaches of DT, LR, ANN, SVM, and RF employed in this investigation. GBM is a contemporary and state‐of‐the‐art ML method, which was used to recognize a set of important predictors that may help associate TIA/stroke and all‐cause mortality with MS and AFL (Cao et al, 2019 ). In previous investigations, GBM models have been used successfully for genetic disease predictions, high blood pressure prediction, and cardiovascular disease progression predictions (Chang et al, 2019 ; O'Driscoll et al, 2021 ; Shumake et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach

Rauf,

Ullah,

Rathi

et al. 2023

Noninvasive Electrocardiol

View full text Add to dashboard Cite

BackgroundOur study hypothesized that an intelligent gradient boosting machine (GBM) model can predict cerebrovascular events and all‐cause mortality in mitral stenosis (MS) with atrial flutter (AFL) by recognizing comorbidities, electrocardiographic and echocardiographic parameters.MethodsThe machine learning model was used as a statistical analyzer in recognizing the key risk factors and high‐risk features with either outcome of cerebrovascular events or mortality.ResultsA total of 2184 patients with their chart data and imaging studies were included and the GBM analysis demonstrated mitral valve area (MVA), right ventricular systolic pressure, pulmonary artery pressure (PAP), left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and surgery as the most significant predictors of transient ischemic attack (TIA/stroke). MVA, PAP, LVEF, creatinine, hemoglobin, and diastolic blood pressure were predictors for all‐cause mortality.ConclusionThe GBM model assimilates clinical data from all diagnostic modalities and significantly improves risk prediction performance and identification of key variables for the outcome of MS with AFL.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Paths are tagged by colors (eg red for negative and blue for positive). Multidimensional clustering was used to determine the proximity of the points (Cao et al, 2019 ). In this investigation, we calculated the correlation coefficient to obtain the correlation matrix of each variable pair, forming a chord diagram.…”

Section: Methodsmentioning

confidence: 99%

Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach

Rauf,

Ullah,

Rathi

et al. 2023

Noninvasive Electrocardiol

View full text Add to dashboard Cite

show abstract

“…This section thus revels the effectiveness of MHE in characterizing the topology of decision boundaries. Following the cluster boundary detection [58] and out-of-distribution detection [59], we also use a hyper-parameterized threshold to divide one cluster into two parts: 1) in-version-space i.e. tube manifold M, and 2) out-version-space i.e.…”

Section: Data-efficient Version Space Representationmentioning

confidence: 99%

Data-Efficient Learning via Minimizing Hyperspherical Energy

Cao¹,

Liu²,

Tsang³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Deep learning on large-scale data is dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces for the success of deep learning. However, there still exist scenarios where collecting data or labels could be extremely expensive, e.g., medical imaging and robotics. To fill up this gap, this paper considers the problem of data-efficient learning from scratch using a small amount of representative data. First, we characterize this problem by active learning on homeomorphic tubes of spherical manifolds. This naturally generates feasible hypothesis class. With homologous topological properties, we identify an important connection -finding tube manifolds is equivalent to minimizing hyperspherical energy (MHE) in physical geometry. Inspired by this connection, we propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL, covering convergence and generalization analysis. Finally, we demonstrate the empirical performance of MHEAL in a wide range of applications on data-efficient learning, including deep clustering, distribution matching, version space sampling and deep active learning.

show abstract

“…Based on the work of Cao et al (2018), noisy perturbations around the boundary usually are characterized with low density observations. We thus estimate the density of the data constrained within a fixed hypersphere…”

Section: Approximating D Into D Using Poincaré Distancementioning

confidence: 99%

Distribution Matching for Machine Teaching

Cao¹,

Tsang²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

You may need to know the following questions before reading our manuscript.Q1: What is machine teaching? Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the student's learning parameters. In simple terms, machine teaching can help to find the best training data for student learners automatically.Q2: What is our research problem? Previous machine teaching studies on machine teaching focused on balancing the teaching risk and cost to find those best teaching examples deriving the student model. This optimization solver is in general ineffective when the student learner does not disclose any cue (i.e. a black-box) of the learning parameters. To supervise such a teaching scenario, this paper presents a distribution matching-based machine teaching strategy.Q3: Why it is significant? Our study will help machine learning to find the best teaching examples on a blackbox setting, then the training examples will be closed-form. In this paper, we present a distribution matching perspective to resolve this issue from optimization.Q4: What is our novelty? Technically, our strategy can be expressed as a cost-controlled optimization process that finds the optimal teaching examples without further exploring in the parameter distribution of the student learner.Q5: What is the related study? The most relevant machine learning study to machine teaching is active learning. Its key assumption is that a student learner who frequently interacts with a teacher (annotator) would do better or no worse than other passive student learners who randomly solicit the training examples. Generally, active learning forwardly updates the current training model into its target, while the target is always agnostic.

show abstract

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Cited by 8 publications

References 65 publications

Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach

Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach

Data-Efficient Learning via Minimizing Hyperspherical Energy

Distribution Matching for Machine Teaching

Contact Info

Product

Resources

About