An optimized dimensionality reduction model for high-dimensional data based on Restricted Boltzmann Machines

Zhang, Ke; Liu, Jianhuan; Chai, Yi; Qian, Kun

doi:10.1109/ccdc.2015.7162428

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…The RBM was invented by Hinton in 2007 for learning a probability distribution over its set of inputs [ 95 ]. It is a generative stochastic artificial neural network that has wide applications in different areas such as dimensionality reduction [ 96 ], classification [ 97 ], regression [ 98 ], collaborative filtering [ 99 ], feature learning [ 100 ], and topic modeling [ 101 ].…”

Section: Deep Learning Models and Methodsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

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Section: Deep Learning Models and Methodsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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“…Different methods for the feature selection have been mentioned in the published references such as multidimensional scaling [76], independent component analysis [77], latent semantic indexing [78], and partial least square [79], and PCA [80]. The related literature review showed that PCA is one of the best statistical techniques for feature reduction [81][82][83][84][85]. In this study, PCA was used to extract the principal components (PCs), to be used as predictors in order to make an ANN model more effective in predicting the energy output of Iranian tea production.…”

Section: Principal Component Analysismentioning

confidence: 99%

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

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There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019

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“…The typical Euclidean distance for similarity measure is inefficient when the number of variables is large, and the number of samples is relevantly small [3]; 2. The computational complexity of the algorithm increases with the number of dimensions [4]; and 3. It is difficult to determine the cluster centroids if the data values are sparse, i.e., only a small number of data entries having a non-null value.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Causal Attributions of Breast Cancer

Chen

Hajderanj

Mallet³

et al. 2021

Lecture Notes in Networks and Systems

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In this paper, a deep learning-based approach is applied to high dimensional, high-volume, and high-sparsity medical data to identify critical casual attributions that might affect the survival of a breast cancer patient. The Surveillance Epidemiology and End Results (SEER) breast cancer data is explored in this study. The SEER data set contains accumulated patient-level and treatment-level information, such as cancer site, cancer stage, treatment received, and cause of death. Restricted Boltzmann machines (RBMs) are proposed for dimensionality reduction in the analysis. RBM is a popular paradigm of deep learning networks and can be used to extract features from a given data set and transform data in a non-linear manner into a lower dimensional space for further modelling. In this study, a group of RBMs has been trained to sequentially transform the original data into a very low dimensional space, and then the k-means clustering is conducted in this space. Furthermore, the results obtained about the cluster membership of the data samples are mapped back to the original sample space for interpretation and insight creation. The analysis has demonstrated that essential features relating to breast cancer survival can be effectively extracted and brought forward into a much lower dimensional space formed by RBMs.

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An optimized dimensionality reduction model for high-dimensional data based on Restricted Boltzmann Machines

Cited by 8 publications

References 22 publications

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep Learning in Diverse Intelligent Sensor Based Systems

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Deep Learning Causal Attributions of Breast Cancer

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