Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss framework

Ahmadkhani, Somaye; Adibi, Peyman

doi:10.1049/iet-cvi.2014.0434

Cited by 24 publications

(12 citation statements)

References 26 publications

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“…In this study, we have applied the principal component analysis (PCA) method which provides linear mapping based on an eigenvector search. PCA provides different approaches to reduce the feature space dimensionality [35,36]. In this study, the dataset is split into 70 : 30 ratio, i.e., 70% of the data is used for training, while 30% is used for the testing purpose.…”

Section: Methodsmentioning

confidence: 99%

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Tariq

Hanif

Sarwar

et al. 2021

Security and Communication Networks

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Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.

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Section: Methodsmentioning

confidence: 99%

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Tariq

Hanif

Sarwar

et al. 2021

Security and Communication Networks

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“…Preserving as much variability as possible refers to the discovery of new variables that are linear functions of those in the original dataset. These linear functions maximize the variance and are also uncorrelated with each other [26]. Literature on PCA dates back from [8] and also [9] who coined the term principal components.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Review of Dimension Reduction Methods

Nanga¹,

Bawah²,

Acquaye³

et al. 2021

JDAIP

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Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR tech

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“…Zhao et al [13] suggested a technique for dimensional reduction to the use of a spectral-space-based classification (SSFC) device to reduce the spectral dimensions. Typically, in a random way the most complicated information was taken using the Convolutional Neural Network (CNN) technique.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem

Salam¹,

Azar²,

Elgendy³

et al. 2021

IJACSA

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In most conditions, it is a problematic mission for a machine-learning model with a data record, which has various attributes, to be trained. There is always a proportional relationship between the increase of model features and the arrival to the overfitting of the susceptible model. That observation occurred since not all the characteristics are always important. For example, some features could only cause the data to be noisier. Dimensionality reduction techniques are used to overcome this matter. This paper presents a detailed comparative study of nine dimensionality reduction methods. These methods are missing-values ratio, low variance filter, highcorrelation filter, random forest, principal component analysis, linear discriminant analysis, backward feature elimination, forward feature construction, and rough set theory. The effects of used methods on both training and testing performance were compared with two different datasets and applied to three different models. These models are, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest classifier (RFC). The results proved that the RFC model was able to achieve the dimensionality reduction via limiting the overfitting crisis. The introduced RFC model showed a general progress in both accuracy and efficiency against compared approaches. The results revealed that dimensionality reduction could minimize the overfitting process while holding the performance so near to or better than the original one.

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Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss framework

Cited by 24 publications

References 26 publications

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Review of Dimension Reduction Methods

The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem

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