排序方式: 共有10条查询结果,搜索用时 15 毫秒
1
1.
2.
Constrained Latent Class Analysis of Three-Way Three-Mode Data 总被引:1,自引:1,他引:0
3.
4.
The additive biclustering model for two-way two-mode object by variable data implies overlapping clusterings of both the objects and the variables together with a weight for each bicluster (i.e., a pair of an object and a variable cluster). In the data analysis, an additive biclustering model is fitted to given data by means of minimizing a least squares loss function. To this end, two alternating least squares algorithms (ALS) may be used: (1) PENCLUS, and (2) Baier’s ALS approach. However, both algorithms suffer from some inherent limitations, which may hamper their performance. As a way out, based on theoretical results regarding optimally designing ALS algorithms, in this paper a new ALS algorithm will be presented. In a simulation study this algorithm will be shown to outperform the existing ALS approaches. 相似文献
5.
To reveal the structure underlying two-way two-mode object by variable data, Mirkin (1987) has proposed an additive overlapping clustering model. This model implies an overlapping clustering of the objects and a reconstruction of the data, with the reconstructed variable profile of an object being a summation of the variable profiles of the clusters it belongs to. Grasping the additive (overlapping) clustering structure of object by variable data may, however, be seriously hampered in case the data include a very large number of variables. To deal with this problem, we propose a new model that simultaneously clusters the objects in overlapping clusters and reduces the variable space; as such, the model implies that the cluster profiles and, hence, the reconstructed data profiles are constrained to lie in a lowdimensional space. An alternating least squares (ALS) algorithm to fit the new model to a given data set will be presented, along with a simulation study and an illustrative example that makes use of empirical data. 相似文献
6.
7.
Robustness analysis of leader-follower consensus 总被引:1,自引:0,他引:1
In this paper, robustness properties of the leader-follower consensus are considered. For simplicity of presentation, the
attention is focused on a group of continuous-time first-order dynamic agents with a time-invariant communication topology
in the presence of communication errors. In order to evaluate the robustness of leader-follower consensus, two robustness
measures are proposed: the L
2 gain of the error vector to the state of the network and the worst case L
2 gain at a node. Although the L
2 gain of the error vector to the state of the network is widely used in robust control design and analysis, the worst case
L
2 gain at a node is less conservative with respect to the number of nodes in the network. It is thus suggested that the worst
case L
2 gain at a node is used when the robustness of consensus is considered. Theoretical analysis and simulation results show that
these two measures are sensitive to the communication topology. In general, the “optimal” communication topology that can
achieve most robust performance with respect to either of the proposed robustness measures is difficult to characterize and/or
obtain. When the in-degree of each follower is one, it is shown that both measures reach a minimum when the leader can communicate
to each node in the network.
This work is supported by the National Natural Science Foundation of China under Grant No. 60774005. 相似文献
8.
We propose a non-negative real-valued model of hierarchical classes (HICLAS) for two-way two-mode data. Like the other members
of the HICLAS family, the non-negative real-valued model (NNRV-HICLAS) implies simultaneous hierarchically organized classifications
of all modes involved in the data. A distinctive feature of the novel model is that it yields continuous, non-negative real-valued
reconstructed data, which considerably expands the application range of the HICLAS family. The expansion implies a major algorithmic
challenge as it involves a move from the typical discrete optimization problems in HICLAS to a mixed discrete-continuous one.
To solve this mixed discrete-continuous optimization problem, a two-stage algorithm combining a simulated annealing and an
alternating local descent stage is proposed. Subsequently it is evaluated in a simulation study. Finally, the NNRVHICLAS model
is applied to an empirical data set on anger. 相似文献
9.
Block-Relaxation Approaches for Fitting the INDCLUS Model 总被引:1,自引:1,他引:0
A well-known clustering model to represent I?×?I?×?J data blocks, the J frontal slices of which consist of I?×?I object by object similarity matrices, is the INDCLUS model. This model implies a grouping of the I objects into a prespecified number of overlapping clusters, with each cluster having a slice-specific positive weight. An INDCLUS model is fitted to a given data set by means of minimizing a least squares loss function. The minimization of this loss function has appeared to be a difficult problem for which several algorithmic strategies have been proposed. At present, the best available option seems to be the SYMPRES algorithm, which minimizes the loss function by means of a block-relaxation algorithm. Yet, SYMPRES is conjectured to suffer from a severe local optima problem. As a way out, based on theoretical results with respect to optimally designing block-relaxation algorithms, five alternative block-relaxation algorithms are proposed. In a simulation study it appears that the alternative algorithms with overlapping parameter subsets perform best and clearly outperform SYMPRES in terms of optimization performance and cluster recovery. 相似文献
10.
k . In this procedure, a least-squares loss function in terms of discrepancies between D and M is minimized. The present paper
describes the original hierarchical classes algorithm proposed by De Boeck and Rosenberg (1988), which is based on an alternating
greedy heuristic, and proposes a new algorithm, based on an alternating branch-and-bound procedure. An extensive simulation
study is reported in which both algorithms are evaluated and compared according to goodness-of-fit to the data and goodness-of-recovery
of the underlying true structure. Furthermore, three heuristics for selecting models of different ranks for a given D are
presented and compared. The simulation results show that the new algorithm yields models with slightly higher goodness-of-fit
and goodness-of-recovery values. 相似文献
1