This paper introduces a constrained version of the recently proposed set-membership affine projection algorithm based on the set-membershp criteria for coefficient update. The algorithm is suitable for linearly-constrained minimum-varianc filtering applications. The data selective property of the proposed algorithm greatly reduces the computational burden as compared with a nonselective approac...

Principal component regression (PCR) is often used in regression with multicollinearity. Although this method avoids the problems which can arise in the least squares (LS) approach, it is not optimized with respect to the ability to predict the response variable. We propose a method which combines the two steps in the PCR procedure, namely finding the principal components (PCs) and regression o...

In this paper, an intermittent update interval for filter coefficients and a simplified output error vector computation is proposed for a proportionate affine projection algorithm. It is shown that the proposed algorithm has good convergence performance and much smaller computation complexity than other proportionate-type APAs. Also, the accuracy of its implementation using the logarithmic numb...

In 2001 Buhler and Tompa [BT01] introduced their Projection algorithm for solving the motif discovery problem for motifs of fixed length l. The algorithm finds good candidates for local optimisation by projecting all l-mers present in the input onto a smaller subspace and analysing dense points in that subspace. We have developed Aggregation, a modified version of Projection that analyses dense...

It is well known that the gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this article, we first provide an alternative averaged mapping approach to the GPA. This approach is operator-oriented in nature. Since, in general, in infinite-dimensional Hilbert spaces, GPA has only weak convergence, we provide two modifications of GPA...

Discovery of sequential patterns is becoming increasingly useful and essential in many scienti c and commercial domains. Enormous sizes of available datasets and possibly large number of mined patterns demand e cient and scalable algorithms. In this paper we present two parallel formulations of a serial sequential pattern discovery algorithm based on tree projection that are well suited for dis...