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Low-Rank Representation (LRR) is an effective self-expressiveness method, which uses the observed data itself as the dictionary to reconstruct the original data. LRR focuses on representing the global low-dimensional information, but ignores the real fact that data often resides on low-dimensional manifolds embedded in a high-dimensional data. Therefore, LRR can not capture the non-linear geometric...
Traditional hierarchical clustering (HC) methods are not scalable with the size of databases. To address this issue, a series of summarization techniques, i.e. data bubbles (DB) and its improved versions, have been proposed to compress very large databases into representative seed points suitable for subsequent hierarchy construction. However, DB and its variants have two common drawbacks: 1) their...
Boosting-based methods are effective for class imbalance problem, where the numbers of samples in two or more classes are severely unequal. However, the classifier weights of existing boosting-based methods are calculated by minimizing the error rate, which is inconsistent with the objective of class imbalance learning. As a result, the classifier weights cannot represent the performance of individual...
In this paper, we address an exemplar-based hidden markov model (HMM) that represents the lip motion activity using visual cues for lipreading. The discriminative visual features including the geometric shape parameters and contour-constrained spatial histogram are selected for representing each lip frame. Then, a set of exemplars associated with the HMM is learned jointly to serve as a typical representation...
This paper presents a multi-boosted Hidden Markov Model (HMM) approach to lip password (i.e. the password embedded in the lip motion) based speaker verification, where the speaker is verified by both of lip password and the underlying characteristics of lip motions. That is, the target speaker saying the wrong password or an impostor even knowing the correct password will be detected as well. To this...
The existing works on writer identification consider global feature or local feature, respectively, but not both. Actually, both of global and local features provide the useful information for writer identification. Hence, this paper proposes a method for writer identification by using a mixture of global feature and local feature. In implementation, we utilize 2-D Gabor transformation as the global...
To effectively classify infrared spectrum (IRS) fingerprints of Chinese herbs, this paper presents a new radial basis function (RBF) network namely, Locally Gaussian Mixture based RBF (LGM-RBF) Network. Unlike the traditional RBF network, the LGM-RBF has a mix layer between the hidden layer and the output layer. The hidden nodes with spherical Gaussian are initially grouped so that each group is corresponding...
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