Feature Extraction for Change-Point Detection Using Stationary Subspace Analysis
نویسندگان
چکیده
منابع مشابه
Change-Point Detection using Krylov Subspace Learning
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace learning works well both in matrix compression and implicit calculation of the inner product by taking full advantage of the arbitrariness of the seed vector. We apply our algorithm to a PCA-based change-point detecti...
متن کاملCopy Move Image Forgery Detection Using SURF Feature Point Extraction
The talk about image forgery is very common in the digital image forensic area. But with advanced image editing tools exposure of tampered part from the original image is hard. Human cannot visually identify the fabricated region from the Image. So, it is imperative to advance a technique which can detect the forged image from the original one. Copy-move/paste image forgery is a frequent catego...
متن کاملWater Feature Extraction and Change Detection Using Multitemporal Landsat Imagery
Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 ...
متن کاملA Subspace Kernel for Nonlinear Feature Extraction
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used preprocessing step in pattern classification and data mining tasks. Given a positive definite kernel function, it is well known that the input data are implicitly mapped to a feature space with usually very high dimensionality. The goal of KFE is to find a low dimensional subspace of this feature space,...
متن کاملNonlinear and Non-stationary Vibration Analysis for Mechanical Fault Detection by Using EMD-FFT Method
The Hilbert-Huang transform (HHT) is a powerful method for nonlinear and non-stationary vibrations analysis. This approach consists of two basic parts of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). To achieve the reliable results, Bedrosian and Nuttall theorems should be satisfied. Otherwise, the phase and amplitude functions are mixed together and consequently, the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2012
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2012.2185811