The problem of signal representation in terms of basis vectors from a large, overcomplete,
spanning dictionary has been the focus of much research. Achieving a succinct, orsparse' …

We describe and evaluate three forward sequential basis selection methods: basic matching
pursuit (BMP), order recursive matching pursuit (ORMP) and modified matching pursuit …

Selection of a subset of vectors from a larger dictionary of vectors arises in a wide variety of
application areas. This problem is known to be NP-hard and many algorithms have been …

The time-frequency and time-scale communities have recently developed an enormous
number of over-complete signal dictionaries, wavelets, wavelet packets, cosine packets …

The problem of optimal approximation of members of a vector space by a linear combination
of members of a large overcomplete library of vectors is of importance in many areas …

The Time-Frequency and Time-Scale communities have recently developed a large number
of overcomplete waveform dictionaries. Decomposition into overcomplete systems is not …

We introduce greedy basis pursuit (GBP), a new algorithm for computing sparse signal
representations using overcomplete dictionaries. GBP is rooted in computational geometry …

An adaptive procedure for signal representation is proposed. The representation is built up
through functions (atoms) selected from a redundant family (dictionary). At each iteration, the …

This paper introduces the concept of sensing dictionaries. It presents an alteration of greedy
algorithms like thresholding or (orthogonal) matching pursuit which improves their …

A novel approach to template matching using nonorthogonal expansion with template-
similar basis functions is described. The results show that this expansion is superior in …