Non-negative Matrix Factorization (NMF) is one latest presented approach for obtaining document clusters, which aimed to provide a minimum error non-negative representation of the term-document matrix. In this paper, ...
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Ray-tracing, can produce high-quality images, however, the use of ray-tracing has been limited due to its high demands on computational power and memory bandwidth, especially in the case of satellite imagery. In this ...
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Ensemble learning with output from multiple supervised and unsupervised models aims to improve the classification accuracy of supervised model ensemble by jointly considering the grouping results from unsupervised mod...
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ISBN:
(纸本)9781577355120
Ensemble learning with output from multiple supervised and unsupervised models aims to improve the classification accuracy of supervised model ensemble by jointly considering the grouping results from unsupervised models. In this paper we cast this ensemble task as an unconstrained probabilistic embedding problem. Specifically, we assume both objects and classes/clusters have latent coordinates without constraints in a D-dimensional Euclidean space, and consider the mapping from the embedded space into the space of results from supervised and unsupervised models as a probabilistic generative process. The prediction of an object is then determined by the distances between the object and the classes in the embedded space. A solution of this embedding can be obtained using the quasi-Newton method, resulting in the objects and classes/clusters with high co-occurrence weights being embedded close. We demonstrate the benefits of this unconstrained embedding method by three real applications.
The automatic Web services composition has been a research focus since an ever-increasing numbers of Web services are created and published. In this paper, we present a dynamic description logics (DDLs) based method f...
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The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, i...
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The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, in spin-52 and spin-32 systems. It is, of course, desirable to have a natural resource state in a spin-12, that is, qubit system. Here, we give a negative answer to this question for frustration-free systems with two-body interactions. In fact, it is shown to be impossible for any genuinely entangled qubit state to be a nondegenerate ground state of any two-body frustration-free Hamiltonian. What is more, we also prove that every spin-12 frustration-free Hamiltonian with two-body interaction always has a ground state that is a product of single- or two-qubit states. In other words, there cannot be any interesting entanglement features in the ground state of such a qubit Hamiltonian.
This paper proposes a novel face recognition algorithm inspired by the selective attention of Human Visual System (HVS). We record four observers' eye movements when they are viewing 100 FRGC [1] frontal view face...
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Rough set theory proposed by Pawlak, is a complementary generalization of classical set theory. The relations between rough sets and algebraic systems endowed with two binary operations such as rings, groups and semig...
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MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by th...
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In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image pat...
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Vladimir N. Vapnik. (1998) pointed out that maxlikelihood functions in EM algorithms are just a special risk function. Based on this opinion, a novel EM algorithm uses a risk function differ with maxlikelihood functio...
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Vladimir N. Vapnik. (1998) pointed out that maxlikelihood functions in EM algorithms are just a special risk function. Based on this opinion, a novel EM algorithm uses a risk function differ with maxlikelihood functions, in stead, a risk formula based on the least square method is used. The gradient descending approach should be used in such kind approaches. Such kind EM algorithms can estimate the parameters of a random model from both labeled and unlabeled samples, and are suitable for semi-supervised learning.
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