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This paper presents the sliding exponential window max-correlation matching (SEWMCM) adaptive algorithm and the sliding rectangular window max-correlation matching (SRWMCM) adaptive algorithm for finding the maximum correlation of two different signal vectors. A unified approach to the steady-state excess mean square error (MSE) performance analyses for proposed algorithms is developed, including...
Subband based blind source separation (BSS) has a great potential in solving the complicated convolutive mixing problems. However, its performance is largely affected by the permutation ambiguity problem during the synthesis stage. Researchers have suggested methods to correct the permutation by using the correlation information between adjacent frequencies/subbands. In this paper, we propose an improved...
Currently, most recommender systems are using collaborative filtering (CF) techniques. The main idea is to suggest new relevant items for an active user based on the judgements from other members in the like-minded community. However, these CF-based methods encounter the obstacles, such as sparse data, cold-start and robustness. This paper proposes to deal with these issues by associating similarity...
Similarity-based collaborative filtering systems are vulnerable to the data sparsity, cold-start, and robustness problems. Computational trust models are promising alternative solutions to alleviate these problems by replacing similarity metric with trust metric. However, they often have some shortages that rely on users' explicit trust statements. A fine-grained model computing trust from user ratings...
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