Sliding Window Adaptive SVD Algorithms
نویسندگان
چکیده
منابع مشابه
Sliding window adaptive fast QR and QR-lattice algorithms
Sliding window formulations of the fast QR and fast QR-lattice algorithms are presented. The derivations are based on the partial triangularization of raw data matrices. Three methods for window downdating are discussed: the method of plane hyperbolic rotations, the Chambers’ method, and the LINPACK algorithm. A numerically ill-conditioned stationary signal and a speech signal are used in finit...
متن کاملRandomized sliding window algorithms for regular languages
A sliding window algorithm receives a stream of symbols and has to output at each time instant a certain value which only depends on the last $n$ symbols. If the algorithm is randomized, then at each time instant it produces an incorrect output with probability at most $\epsilon$, which is a constant error bound. This work proposes a more relaxed definition of correctness which is parameterized...
متن کاملThe Imaginary Sliding Window As a New Data Structure for Adaptive Algorithms
Abstract.1 The scheme of the sliding window is known in Information Theory, Computer Science, the problem of predicting and in stastistics. Let a source with unknown statistics generate some word . . . x−1x0x1x2 . . . in some alphabet A. For every moment t, t = . . . −1, 0, 1, . . ., one stores the word (”window”) xt−wxt−w+1 . . . xt−1 where w,w ≥ 1, is called ”window length”. In the theory of ...
متن کاملNormalized Sliding Window Constant Modulus Algorithms for Blind Equalization
Cette communication pr esente une nouvelle classe d'algorithmes d' egalisation aveugle pour la transmission des donn ees MAP ou MAQ a travers un canal de communication a phase non-minimale. Cette classe d'algorithmes est deriv ee en minimisant un crit ere d eterministe qui impose un ensemble de contraintes bas ees sur la propriet e du module constant de la constellation emise et permet l'accel ...
متن کاملIncremental and Adaptive Clustering Stream Data over Sliding Window
Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since system resources are bounded whilst the data arrive at the system unboundedly and in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model in which the re...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2004
ISSN: 1053-587X
DOI: 10.1109/tsp.2003.820069