Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis

Adaptive Kernel Principal Analysis for Online Feature Extraction

The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated t...

Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis

BACKGROUND The electroencephalography (EEG) signals are known to involve the firings of neurons in the brain. The P300 wave is a high potential caused by an event-related stimulus. The detection of P300s included in the measured EEG signals is widely investigated. The difficulties in detecting them are that they are mixed with other signals generated over a large brain area and their amplitudes...

A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis

In recent years, as one of the biometric identification technology, palm-print identification has received many reseachers’ attention. To solve the key problem of palm-print recognition -feature extraction, we propose a new method, which based on wavelet transform and principal component analysis. In general, we use wavelet transform to deal with palm print images and extract high-dimensional w...

Real Time On-chip Sequential Adaptive Principal Component Analysis for Data Feature Extraction and Image Compression

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principal Component Analysis (PCA) which will “help optimize the hardware implementation” in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation. To demonstrate the performance of our algorithm, we study...