Compressed Matching in Dictionaries
نویسندگان
چکیده
The problem of compressed pattern matching, which has recently been treated in many papers dealing with free text, is extended to structured files, specifically to dictionaries, which appear in any full-text retrieval system. The prefix-omission method is combined with Huffman coding and a new variant based on Fibonacci codes is presented. Experimental results suggest that the new methods are often preferable to earlier ones, in particular for small files which are typical for dictionaries, since these are usually kept in small chunks.
منابع مشابه
Orthogonal Matching Pursuit with random dictionaries
In this paper we investigatet the efficiency of the Orthogonal Matching Pursuit for random dictionaries. We concentrate on dictionaries satisfying Restricted Isometry Property. We introduce a stronger Homogenous Restricted Isometry Property which is satisfied with overwhelming probability for random dictionaries used in compressed sensing. We also present and discuss some open problems about OMP.
متن کاملFrames for compressed sensing using coherence
We give some new results on sparse signal recovery in the presence of noise, for weighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, for random dictionaries this condition is rarely satised. Moreover, we give better estimations then the ones given recently by Cai, Wang and Xu.
متن کاملINTERDISCIPLINARY MATHEMATICS INSTITUTE 2010 : 01 Orthogonal super greedy algorithm and application in compressed sensing IMI
The general theory of greedy approximation is well developed. Much less is known about how specific features of a dictionary can be used for our advantage. In this paper we discuss incoherent dictionaries. We build a new greedy algorithm which is called the Orthogonal Super Greedy Algorithm (OSGA). OSGA is more efficient than a standard Orthogonal Greedy Algorithm (OGA). We show that the rates ...
متن کاملSearching in Compressed Dictionaries
The problem of Compressed Pattern Matching , introduced by Amir and Benson [1], is of performing pattern matching directly in a compressed text without any decompressing. More formally, for a given text T , pattern P and complementary encoding and decoding functions E and D, respectively, our aim is to search for E(P ) in E(T ), rather than the usual approach which searches for the pattern P in...
متن کاملLearning of Graph Compressed Dictionaries for Sparse Representation Classification
Despite the limited target data available to design face models in video surveillance applications, many faces of non-target individuals may be captured in operational environments, and over multiple cameras, to improve robustness to variations. This paper focuses on Sparse Representation Classification (SRC) techniques that are suitable for the design of still-to-video FR systems based on unde...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Algorithms
دوره 4 شماره
صفحات -
تاریخ انتشار 2011