An Improved Method of Training Overcomplete Dictionary Pair
نویسندگان
چکیده
منابع مشابه
Stochastic Expansions in an Overcomplete Wavelet Dictionary
We consider random functions defined in terms of members of an overcomplete wavelet dictionary. The function is modelled as a sum of wavelet components at arbitrary positions and scales where the locations of the wavelet components and the magnitudes of their coefficients are chosen with respect to a marked Poisson process model. The relationships between the parameters of the model and the par...
متن کاملOvercomplete Dictionary Design by Empirical Risk Minimization
Recently, there have been a growing interest in application of sparse representation for inverse problems. Most studies concentrated in devising ways for sparsely representing a solution using a given prototype overcomplete dictionary. Very few studies have addressed the more challenging problem of construction of an optimal overcomplete dictionary, and even these were primarily devoted to the ...
متن کاملA Shift Tolerant Dictionary Training Method
Traditional dictionary learning method work by vectorizing long signals, and training on the frames of the data, thereby restricting the learning to time-localized atoms. We study a shift-tolerant approach to learning dictionaries, whereby the features are learned by training on shifted versions of the signal of interest. We propose an optimized Subspace Clustering learning method to accommodat...
متن کاملSparse Signal Recovery with Dynamic Update of Overcomplete Dictionary
Sparse signal priors help in a variety of modern signal processing tasks. In a typical sparse recovery problem, a sparse signal needs to be recovered from an underdetermined system of equations. For example, sparse representation of signal in an overcomplete dictionary or reconstruction of a sparse signal from a small number of linear measurements. In recent years, several results have been pre...
متن کاملAn improved method for unsupervised training of LVCSR systems
In this paper, we introduce an improved method for unsupervised training where the data selection or filtering process is done on state level. We describe in detail the setup of the experiments and introduce the state confidence scores on word and allophone state level for performing the data selection for mixture training on state level. Although we are using a relatively small amount of 180 h...
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: Mathematical Problems in Engineering
سال: 2014
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2014/386835