Learn More
Performance of search during matching phase in a speaker identification system realized through vector quantization (VQ) is investigated in this paper. Voice of each person is recorded in a office room with personal computers. LPC−cepstrum is selected as feature vector. In order to gain higher success rate of identification, it is necessary to use larger(More)
The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization(More)
The encoding process of vector quantization (VQ) is very heavy and it constrains VQ's application to a great deal. In order to speed up VQ encoding, it is most important to avoid unnecessary Euclidean distance computation (k-D) as much as possible by the difference check first that uses simpler features (low dimensional) while winner searching is going on.(More)
Vector quantization (VQ) is an asymmetric coding method and the winner search in encoding process is extremely time-consuming. This property of VQ constrains its practical applications very much. Based on the sum pyramid data structure of a vector, a fast encoding algorithm with PSNR equivalent to full search is proposed in this paper to improve the results(More)
This paper proposes a new greedy algorithm combining the semi-supervised learning and the sparse representation with the data-dependent hypothesis spaces. The proposed greedy algorithm is able to use a small portion of the labeled and unlabeled data to represent the target function, and to efficiently reduce the computational burden of the semi-supervised(More)