This paper develops a distributed dictionary learning algorithm for sparse representation of
the data distributed across nodes of sensor networks, where the sensitive or private data are …

We address the problem of scheduling test sessions for core based systems-on-chip (SOC).
We assume the built-in self-test methodology for testing individual cores and permit sharing …

This work aims at discovering, in an unsupervised fashion, the nature of phonemes on the
basis of their distributional information within a representative corpus. We focus on some …

We present an approach for comparing human-made and automatically generated semantic
representations with an assumption that neither of these has a primary status over the other …

Vector space models are used in language processing applications for calculating semantic
similarities of words or documents. The vector spaces are generated with feature extraction …

The concept of style is a characteristic that is somewhat difficult to define or measure
distinctly and is thus far less tangible compared to other possible characteristics. The …

Sparse coding has high-performance encoding and ability to express images, sparse
encoding basis vector plays a crucial role. The computational complexity of the most existing …

Latent semantic analysis (LSA) can be used to create an implicit semantic vectorial
representation for words. Independent component analysis (ICA) can be derived as an …