Online feature extraction based on accelerated kernel principal component analysis for data stream

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...

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis

Kernel Principal Component Analysis

A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal components in high{ dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d{pixel products in images. We give the derivation of the method and present experimenta...

Feature Dimension Reduction of Multisensor Data Fusion using Principal Component Fuzzy Analysis

These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. O...

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...