There are several cutting edge applications needing PCA methods for data on tori and we propose a novel torus-PCA method with important properties that can be generally applied. There are two existing general methods: tangent space PCA and geodesic PCA. However, unlike tangent space PCA, our torus-PCA honors the cyclic topology of the data space whereas, unlike geodesic PCA, our torus-PCA produ...

We propose a method called generalized N-dimensional principal component analysis (GND-PCA) for the modeling of a series of multi-dimensional data in this paper. In this method, the data are directly trained as the higher-order tensor and the bases in each mode subspace are calculated to compactly represent the data. Since GND-PCA analyzes the multi-dimensional data directly on each mode better...

To overcome the shortage of traditional temperature sensors, this paper adopts infrared thermal imaging technology for measurement. avoid spatial information loss issue during image data vectorization process, adopted relationship between pixels in principal component analysis (PCA) model training, which is called information-based PCA (SIPCA). Then, also used fault localization method to enhan...

A PCA based gait segmentation method is proposed. Background images are used for PCA training. PCA reconstruction, recursive error compensation and single threshold based method is put forward for gait segmentation. Single threshold based method has the same gait segmentation ability as classical adaptive threshold based method, and the former is faster in implementation than the later. Propose...

By analyzing the direction characteristic of principal component analysis (PCA), we propose an edge detection method based on PCA. Using Karhunen–Loëve transform, PCA transforms the original dataset into lower-dimensional feature data. The transform has directivity both on energy accumulation and data selection. The author points out and proves the two direction characteristics. In this paper, ...

Principal Component Analysis (PCA) aims to learn compact and informative representations for data and has wide applications in machine learning, text mining and computer vision. Classical PCA based on a Gaussian noise model is fragile to noise of large magnitude. Laplace noise assumption based PCA methods cannot deal with dense noise effectively. In this paper, we propose Cauchy Principal Compo...