Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

You, Shingchern D.; Hung, Ming-Jen

doi:10.3390/info12010001

Cited by 5 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, we have studied the use of PCA (principal component analysis) for reducing the dimensionality of spectral-temporal objects [36], and the results were promising. Later on, we also repeated the experiments by using factor analysis (FA) [36], and found that FA was more robust to distorted objects. So, we would like to investigate if FA is useful in the present case.…”

Section: Dimensionality Reduction By Factor Analysismentioning

confidence: 99%

See 1 more Smart Citation

Classification of Relaxation and Concentration Mental States with EEG

You

2021

Information

Self Cite

View full text Add to dashboard Cite

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

show abstract

Section: Dimensionality Reduction By Factor Analysismentioning

confidence: 99%

“…The only difference is the computation of the matrix for reduction, i.e., Equation (6). As the FA approach used here is an extension of the PCA approach [36], we omit the use of the PCA in this subsection to save space.…”

Section: Dimensionality Reduction By Factor Analysismentioning

confidence: 99%

Classification of Relaxation and Concentration Mental States with EEG

You

2021

Information

Self Cite

View full text Add to dashboard Cite

show abstract

“…By following these steps, PCA effectively reduces the dimensionality of the dataset while preserving the most important information and capturing the most significant variations in the data [62,63].…”

mentioning

confidence: 99%

“…For detailed steps on PCA, please refer to [62,63], and for ICA steps, [63][64][65][66]. These dimension reduction approaches contribute to a faster and more efficient identification of top models in the fusion process.…”

mentioning

confidence: 99%

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

Chauhan,

Isshiki,

2024

Acoustics

View full text Add to dashboard Cite

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies.

show abstract

Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities

Cortés

Porras

Monte

2021

REMEF

View full text Add to dashboard Cite

The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market. The methodology used compares the results of estimation produced by Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Neural Networks Principal Component Analysis (NNPCA) under three different perspectives. The results showed that in general: PCA, FA, and ICA produced similar systematic risk factors and betas; NNPCA and ICA produced the greatest number of fully accepted models in the econometric contrast; and, the interpretation of systematic risk factors across the four techniques was not constant. Additional research testing alternative extraction techniques, econometric contrast, and interpretation methodologies are recommended, considering the limitations derived from the scope of this work. The originality and main contribution of this paper lie in the comparison of these four techniques in both the financial and Mexican contexts. The main conclusion is that depending on the purpose of the analysis, one technique will be more suitable than another.

show abstract

Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

Cited by 5 publications

References 18 publications

Classification of Relaxation and Concentration Mental States with EEG

Classification of Relaxation and Concentration Mental States with EEG

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities

Contact Info

Product

Resources

About