Big data analytics enabled by feature extraction based on partial independence

Ke, Qiao; Zhang, Jiangshe; Song, Houbing; Wan, Yan

doi:10.1016/j.neucom.2017.07.072

Cited by 41 publications

(16 citation statements)

References 30 publications

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“…When the sparsity is above 40 the successful rates of four methods drop rapidly. Our method is a little higher and more stable than the other three methods [5,9,16,18,21,26,28,32].…”

Section: Figurementioning

confidence: 82%

Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Zhou¹,

Wei²,

Woźniak³

et al. 2019

ITC

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It is challenging to recover the required compressed CT (Computed Tomography, CT) image, which is got by transferred through the internet or is stored in a signal library after being compressed. We present a recovery method for compressed sensing CT images. At present, minimizing 0-norm, 1-norm and p-norm is used to recover compressed sensing signals. However, sometimes 0-norm is an NP problem, 1-norm has no solution in theory and p-norm is not a convex function. We introduce a recovery method of compressed sensing signal based on regularized smooth convex optimization. In order to avoid solving the non-convex optimization problems and no solution condition, a convex function is designed as the objective function of optimization to fit 0-norm of signal and a fast iterative shrinkage-thresholding algorithm is proposed to find solution with the convergence speed is quadratic convergence. Experimental results show that our method has a sound recovery effect and is well suitable for processing big data of compressed CT images.

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“…When the sparsity is above 40 the successful rates of four methods drop rapidly. Our method is a little higher and more stable than the other three methods [5,9,16,18,21,26,28,32].…”

Section: Figurementioning

confidence: 82%

Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Zhou¹,

Wei²,

Woźniak³

et al. 2019

ITC

View full text Add to dashboard Cite

show abstract

“…On the contrast, numerous features are prone to train a model with the risk of over-fitting. Therefore, we need to reduce the number of features of mass spectrometry data before using it for cancer detection [20]. Based on PCA method, we plot the first three principle components extracted from samples of three datasets, and these samples with only three principle components are visualized in Fig.…”

Section: E Features Analysismentioning

confidence: 99%

Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

Zhou

Yong

et al. 2020

IEEE Access

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Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis. INDEX TERMS Early cancer diagnosis, deep autoencoder, particle swarm optimization, mass spectrometry feature learning.

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“…New methods of big data analysis have been emerging and predictive accuracy is improving; for example, Ke et al [17] proposed a feature learning algorithm based on the adaptive independent subspace analysis and this method showed higher classification accuracy than the independent component analysis. This computer learning method needs relatively small sample data to predict larger data.…”

Section: Patients' Information Should Include Age Disease Duration mentioning

confidence: 99%

Joint index vector: a novel assessment measure for stratified medicine in patients with rheumatoid arthritis

et al. 2018

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Big data analytics enabled by feature extraction based on partial independence

Cited by 41 publications

References 30 publications

Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

Joint index vector: a novel assessment measure for stratified medicine in patients with rheumatoid arthritis

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