spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...

Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solving PCA, but a number of other algorithms have been proposed. For instance, the EM algorithm is much more efficient in case of high dimensionality and a small number of principal components. We study a case where the data are hi...

Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the ...

We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of this type. MCMC procedures are too slow to be practical in a very high-dimensional setting and standard mean-field variational Bayes algorithms are ineffective. We adopt a dense message passing algorithm similar to algo...

Data collected in realistic mobility traces for mobile ad hoc networks (MANETS) is intrinsically high dimensional. Principal Component Analysis (PCA) is a good tool for reducing the data dimemsion by extracting important features of the data. We propose a method for computing principal components using iterative regression for high dimensional matricies with missing values with an application t...

Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components (PCs) are usually linear combinations of all the original variables, and it is thus often difficult to interpret the PCs. To alleviate this drawback, various sp...