Facial recognition using principal component analysis based dimensionality reduction

Although two-dimensional principal component analysis (2DPCA) extracts image features directly from 2D image matrices rather than one dimensional vectors, 2DPCA is only based on the whole images to preserve total variances by maximizing the trace of feature covariance matrix. Thus, 2DPCA cannot extract localized components, which are usually important for face recognition. Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition called two-dimensional nonnegative principal component analysis (2DNPCA) to extract localized components and maintain the maximal variance property of 2DPCA. 2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, 2DNPCA has both advantages of 2DPCA and NMF. Furthermore, 2DNPCA solves the time-consuming problem by removing the restriction of minimizing the cost function and extracting only the base matrix. The nearest neighbor (NN) classifier and linear regression (LR) classifier are used for classification and extensive experimental results show that 2DNPCA plus NN and 2DNPCA plus LR are both very efficient approaches for face recognition.

An efficient method of face detection based on two-dimensional principal component analysis (PCA) incorporating with support vector machine (SVM) is proposed in this paper. Firstly, a 2DPCA coarse filter with relatively lower computational complexity is applied to the whole input image to filter out most of the non-face, then follows the SVM classifier to make the final decision, so the detection process is speeded up. As opposed to PCA, 2DPCA is based on 2D image matrices rather than ID vector so the image matrix does not need to be transformed into a vector prior to feature extraction. The experiment results show that the method can effectively detect faces under complicated background, and the processing time is shorter than using SVM alone

K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are the two Supervised Machine Learning Algorithms which have been used extensively in solving classification and regression problems in various application domains—medical diagnosis, pattern recognition, and image classification being the popular ones. KNN has been attractive for its simplicity—SVM has been considered as complex. Computational complexity of KNN is less compared to SVM. However, accuracy of SVM in general has been higher compared to KNN. This paper carries out an analysis of complexity of KNN and SVM and then looks at the alternative strategies to reduce the complexity. Accuracies of both SVM and KNN are analyzed from literature survey of experimental results with different training datasets. Reduction of number of training instances and usage of kernel functions are found to be the solutions for reducing SVM complexity. Factors impacting KNN accuracy are analyzed and possible alternatives are assessed. Kernel functions and distance-weighted KNN are concluded as the solution to improve KNN accuracy to compete with SVM accuracy. To utilize the best potential of the two algorithms, the option of a hybrid algorithm combining both KNN and SVM is also proposed.

This study compares the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in predicting customer satisfaction at Warung Makan Indomie (Warmindo). The research process consists of four stages, namely: data collection, data processing, model formation, and model evaluation. This study aims to compare the performance of two classification algorithms, namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), in predicting customer satisfaction levels based on survey data. The evaluation was carried out using accuracy metrics and classification reports to determine the level of precision, recall, and f1-score of each algorithm. The evaluation results show that both algorithms have the same accuracy of 70%. KNN excels in f1-score in class 2 (0.70), while SVM excels in precision in class 2 (0.79). with an average score of both algorithms being 0.61. These results indicate that both KNN and SVM are feasible to use, depending on the performance priority per class

Detection of asphyxia in infant at an early stage is crucial to reduce the rate of infant morbidity. The information regarding asphyxia can be extracted from infant cry signals using support vector machine (SVM) combined with effective feature selection methods such as principal component analysis (PCA) or orthogonal least square (OLS). The performance of SVM in recognizing infant cry with asphyxia after undergone comprehensive identification of optimal parameters at the feature extraction and classification stages has not been reported. This paper describes the two stages of optimal parameter identification; at Mel-frequency Cepstral coefficient (MFCC) analysis stage, SVM with and without employing the PCA and OLS stages, and the performance of the SVM in recognizing infant cry with asphyxia resulted from all levels of optimal parameters identification. The SVM was first optimized after performing MFCC analysis to find the optimum parameters. Two types of kernels were used, the polynomial and RBF kernels. The subsequent SVM optimizations were conducted after PCA and OLS were employed. In the PCA, the significant features were selected using eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE). The SVM performance was evaluated based on classification accuracy and computation time. The experimental results have shown that the optimized SVM was able to recognize the asphyxiated infant cry with an accuracy of 94.84% and computation time of 1.98s using PCA with EOC and RBF kernel.

Air pollution has become one of the biggest environmental challenges worldwide, and Jakarta, the capital city of Indonesia, is no exception. Jakarta, as one of the most populous cities in the world, faces serious problems in ensuring healthy air quality for its residents. The impact of this air pollution is not only limited to human health, but also damages the environment as a whole, including plants and aquatic ecosystems. In an effort to address this issue, many studies have been conducted to develop prediction models that can provide estimates of air quality levels in Jakarta City. Two modeling models that can be used in this context are Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). This study aims to compare the performance of the SVM algorithm model with the KNN algorithm in the classification of air quality in Jakarta City. Data mining is the art and science of discovering knowledge, insights, and patterns in data based on the CRISP -DM (Cross Industry Standard Process For Data Mining) methodology. The data source in this study is the Jakarta air pollution standard index (ISPU) data obtained on the satudata.jakarta.go.id website. This study compares the accuracy of the Jakarta City air quality classification on the Support vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms using python. Comparison results are determined by the level of accuracy and score of the confusion matrix. Classification results are presented in the form of a Graphic User Interface with media interface. The comparison of classification algorithms on two models in data mining shows that the Support Vector Machine (SVM) algorithm is superior to K-Nearest Neighbor (KNN). This is evident from the higher accuracy rate in SVM, especially with the use of the Rbf kernel which reaches 97.05%, compared to KNN which has an accuracy of 94.74% with parameters p = 1 and k = 5. In addition, SVM also shows a higher correct prediction value compared to KNN. In a 1-year time span the overall quality may be quite good, with moderate quality at more than equal to 50 to 100. However, control is still needed for the unhealthy category areas.

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.

The support vector machine (SVM) combined with the K-nearest neighbor (KNN) algorithm is applied to the 3D image recognition algorithm to cope with the issues of 3D image recognition in the image processing process. In this paper, the KNN algorithm is used to extend the eigenvalues of the residual network from frequency to complex plane according to the eigenvalues of 3D images, and the relevant data are extracted from the images by using the corresponding separation rules of the SVM. Subsequently, a sparse observation model is established based on the SVM combined with the KNN, and the 3D image processing issue is converted to a residual network visualization issue of 3D image recognition by establishing a parameterized database. Meanwhile, detection of high-resolution distance of 3D images is conducted, and the variational inference method is used to visualize and analyze the 3D images accordingly. Finally, through the experimental study, it can be known that the method of SVM combined with the KNN algorithm can reach an accuracy of up to 94.18% in the dataset of the Princeton 3D polygonal models. After comparison with the existing SVM-KNN method and deep learning, it can be concluded that the method put forward in this paper has a higher recognition rate and stronger anti-interference capacity, and the algorithm has a superior convergence speed with less adjustment parameters.

In this paper, based on the study of the Two-Dimensional Principal Component Analysis (2DPCA), Two-Dimensional Principal Component Analysis (2DPCA) and fuzzy set theory, we propose a integrated face recognition algorithm based on wavelet subspace. This method can make good use of the advantages of each single method, and also can make up for the defect of each other. The comparison of the results of the different methods identification effect on the ORL、YALE and FERET face database show, the integrated method proposed in this paper improves the recognition rate, and it also reduces the training and classification time as well. Key word: face recognition; Two-Dimensional Principal Component Analysis (2DPCA); Two-Dimensional Linear Discriminant Analysis (2DLDA); fuzzy set theory; feature extraction be divided into several categories:the method based on geometric features、the method based on model、the method based on the statistical、the method based on neural network method and the method of combining multiple classifiers, this paper mainly focuses on the method based on statistics. The method based on statistical faced the image as random vector, thus using some statistical methods to analyze the face model, the most representative methods include the eigenface method based on principal component analysis which proposed by Turk M (2)

DNA Microarrays enable the detection of genetic changes attributable to cancer by simultaneously analyzing the expression of thousands of genes. However, the identification of most relevant genes from thousands of gene expressions available in each biological sample, for cancer classification pose a great challenge. Although researchers have applied BPSO based wrapper approaches to get most relevant genes prior to cancer classification, these approaches didn’t achieve good classification accuracy due to the premature convergence caused by local stagnation problem. This paper proposes an improved Binary Particle Swarm Optimization (iBPSO) to tackle these issues. The proposed iBPSO based wrapper is examined using Naive-Bayes (NB), k-Nearest Neighbor (kNN), and Support Vector Machines (SVM) classifiers with stratified 5-fold cross-validation. The proposed iBPSO exhibited its efficacy in terms of classification accuracy and the number of selected genes in comparison to standard BPSO on six benchmark cancer microarray datasets. Our proposed iBPSO also effectively escapes from local minima stagnation.

Emotion plays a very significant role in human's everyday communication. Emotion recognition is an essential activity for a computer to understand the human inner state in the Internet of things (IoT)-based brain–computer interface (BCI) framework. Researchers nowadays are more intent toward emotion detection from electroencephalogram (EEG) signals. In recent years, many researchers have conducted various methods of emotion detection from physiological (EEG) signals. A variety of features are extracted from the EEG signals, and various classification methods are applied to these features. In this chapter, different classifiers based on machine learning such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gaussian Naive Bayes (GNB) with Principal Component Analysis (PCA) are used to simulate the relationship between emotional states and EEG signals of the brain. DEAP dataset is used to test the efficacy of these machine learning techniques for the analysis of emotions using physiological EEG signals and it is realized that SVM with PCA significantly improves accuracy in cases of valence, arousal, and dominance. In the case of liking, KNN with PCA gives better results in terms of accuracy. It is also found that SVM with PCA provides better classification accuracy i.e. 64.06% for valence, 64.30% for arousal, 69.50% for dominance, and 64.06% for liking, respectively.

Since two-dimensional principal component analysis has been used in face recognition, many approaches in 2D-based method have been developed. However, less attention is spent in the classification methods based on 2D image matrix. Considering that the feature extracted from 2DPCA based is a matrix instead of a single vector as in PCA based, a new measurement distance is proposed which considers the rows of the feature matrix. Unlike the previous methods which are depending on the columns or the whole matrix of the feature matrix, the proposed method is combined with the k-nearest neighbour instead of the 1-nearest neighbour. Moreover, by using the proposed method, the drawback of 2DPCA based algorithms compared to PCA based algorithms, which is the increment of the coefficient numbers, can be alleviated. Experimental results on a famous face databases show that by increasing the number of training images per class, the proposed method accuracy is also increased until it surpasses all methods in terms of accuracy and storage capacity.

Sales data analysis is an important aspect of modern businesses, as it enables them to gain insights into customer preferences and behavior, optimize marketing strategies, and improve revenue. With the rise of big data and machine learning, businesses can now leverage these tools to analyze sales data and gain even deeper insights. This paperfocuses on analyzing sales data using machine learning algorithms, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm. KNN is a popular machine learning algorithm used in classification and regression problems, and it works by identifying the K closest data points to a new observation and predicting its class or value based on the majority class or average value of its nearest neighbors. The dataset used in this paperis from a retail store and includes information on product type, sales channel, customer demographics, and sales volume over a period of several years. The paper will use Python and various machine learning libraries to perform data preprocessing, exploratory data analysis, feature engineering, and modeling. Specifically, KNN will be used to develop a predictive model for sales forecasting, which can help businesses optimize their inventory and production planning. The paper will also use clustering techniques, such as K-Means, to identify customer segments based on their purchasing behavior and demographics, and association rule mining to identify product affinities and recommend complementary products. The results of this analysis can help businesses gain a deeper understanding of their sales data and make data-driven decisions to improve their bottom line. By leveraging the power of KNN and other machine learning algorithms, businesses can gain insights into customer behavior and preferences, optimize their marketing strategies, and improve their overall revenue. Keywords: KNN Algorithm, Data Analytics, Machine Learning, Support Vector Machine.

In this work, we are attempting emotion classification in view of synthesizing story speech. We are proposing emotion-specific text features (ESF) for classifying sentences from children stories into five different emotion categories: happy, sad, anger, fear and neutral. ESF is a five dimensional feature vector, where each dimension corresponds to weight of the sentence according to each emotion class. The dataset consists of 780 Hindi emotional sentences collected from children stories belonging to three genres: fable, folk-tale and legend. Part-of-speech (POS) and proposed ESF are used as features for emotion classification. Emotion classification performance is analysed using various combinations of features with three classifiers: Naive Bayes (NB), k-nearest neighbour (KNN) and support vector machine (SVM). The effectiveness of classifiers is analysed using precision, recall, F-measure and accuracy. The classification performance of 67.9% and 67.2% is achieved using POS and ESF respectively. The fusion of both features resulted an accuracy of 71.1%. Further, the importance of story genre information in emotion classification was observed from the experiments conducted on classifying emotions within story genre. An accuracy of 73.7% was observed after adding story genre information to the fusion of POS and ESF. SVM models outperformed other models in terms of classification accuracy.

The development of online transportation applications such as Maxim has increased the need for sentiment analysis to understand user opinions from reviews on the Google Play Store. The main challenges in this analysis are language diversity, variations in writing style, and data imbalance, which affect model accuracy. This study aims to evaluate the performance of the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) algorithms, as well as ensemble approaches through the Voting Classifier and Combined Classifier, in sentiment analysis of Maxim app reviews. The dataset consists of 2,851 Indonesian-language reviews collected through web scraping from the Google Play Store in 2025. Sentiment labels were automatically determined based on user ratings, where ratings of 4–5 were categorized as positive and ratings below 4 as negative, with an initial distribution of 2,295 positive and 556 negative reviews before balancing using SMOTE–Tomek Links. Preprocessing steps included case folding, tokenization, stopword removal, and stemming using Sastrawi, while feature weighting was performed with unigram TF-IDF. The Combined Classifier merged the probability scores from the SVM, KNN, and RF models to produce the final prediction. Evaluation was conducted using 5-Fold Cross Validation with accuracy, precision, recall, F1-score, and ROC-AUC as evaluation metrics. The results show that RF and the Combined Classifier achieved the best performance with 85% accuracy, 87% precision, 85% recall, 86% F1-score, and 0.91 ROC-AUC, while SVM and the Voting Classifier ranked in the middle and KNN ranked the lowest. These findings confirm that ensemble learning, particularly the Combined Classifier, effectively improves the accuracy and stability of review classification compared to individual methods.