Application of tree-based searches to matching pursuit
نویسندگان
چکیده
Matching Pursuit (MP) uses a greedy search to construct a subset of vectors, from a larger set, which will best represent a signal of interest. Here, we extend this search for the best subset by keeping the K vectors which maximize the selection criterion at each iteration. This is termed the MP:K algorithm and represents a suboptimal search through the tree of all possible subsets where each node is limited to having K children. As a more suboptimal search, we can use the M-L search to select a subset of dictionary vectors, leading to the MP:M-L algorithm. We compare the computation and storage requirements for three variants of the MP algorithm using these searches. Through simulations, the signi cantly improved performance obtained using the MP:K and MP:M-L algorithms is demonstrated. We conclude that the Modi ed Matching Pursuit (MMP) algorithm o ers the best compromise between performance and complexity using these search techniques.
منابع مشابه
PMU-Based Matching Pursuit Method for Black-Box Modeling of Synchronous Generator
This paper presents the application of the matching pursuit method to model synchronous generator. This method is useful for online analysis. In the proposed method, the field voltage is considered as input signal, while the terminal voltage and active power of the generator are output signals. Usually, the difference equation with a second degree polynomial structure is used to estimate the co...
متن کاملWavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure
This paper suggests an upside-down tree-based orthogonal matching pursuit UDT-OMP compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree str...
متن کاملHierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms
Extracting good representations from images is essential for many computer vision tasks. In this paper, we propose hierarchical matching pursuit (HMP), which builds a feature hierarchy layer-by-layer using an efficient matching pursuit encoder. It includes three modules: batch (tree) orthogonal matching pursuit, spatial pyramid max pooling, and contrast normalization. We investigate the archite...
متن کاملTree-based Algorithms for Compressed Sensing with Sparse-Tree Prior
Recent studies have shown that sparse representation can be used effectively as a prior in linear inverse problems. However, in many multiscale bases (e.g. wavelets), signals of interest (e.g. piecewisesmooth signals) not only have few significant coefficients, but also those significant coefficients are well-organized in trees. We propose to exploit this prior, named sparse-tree, for linear in...
متن کاملTop-Down and Bottom-Up Tree Search Algorithms for Selecting Bases in Wavelet Packet Transforms
Search algorithms for finding signal decompositions called near-best bases using decision criteria called non-additive information costs have recently been proposed by Taswell [12] for selecting bases in wavelet packet transforms represented as binary trees. These methods are extended here to distinguish between top-down and bottom-up tree searches. Other new non-additive information cost funct...
متن کامل