We describe methods for learning dictionaries that are appropriate for the representation of
given classes of signals and multisensor data. We further show that dimensionality reduction …

… learning simultaneously a single shared dictionary … to learning, eg, [10, 11, 12, 13, 14], and
in neural networks [15], but not, to the best of our knowledge, in the sparse dictionary learning …

… representation learning paradigms are dictionary learning and deep learning. While … N
representation learning paradigm, dictionary learning has received a lot of interest. Researchers …

… of dictionary learning within the framework of our recently developed log-prior model-based
sparse source vector learning approach that for a known overcomplete dictionary … dictionary …

… dictionary learning algorithms that will be compared. Instead of using the complete dictionary
learning … of these works to be in the dictionary update method, which is incorporated into …

… The dictionary learning method we present in the next section falls into the class of online
algorithms based on stochastic approximations, processing one sample at a time, but exploits …

… The dictionary learning problem has already 20 years of … ways of posing the dictionary
learning problem and on the most … In a strict sense, dictionary learning can be seen as a difficult …

… This classical data-driven approach to dictionary learning is well … Unsupervised dictionary
learning has also been used for other … dictionary learning formulation, which we now present. …

… the dictionary as a whole, thus enforces basic dictionary properties such as mutual coherence
explicitly during the learning … with state-of-the-art dictionary learning methods like K-SVD. …

… the effectiveness of dictionary learning methods in solving several … learning framework
for the task of transforming a dictionary … The dictionary function parameters and domain-invariant …