186,744 research outputs found
Comparative study on the application of evolutionary optimization techniques to orbit transfer maneuvers
Orbit transfer maneuvers are here considered as benchmark cases for comparing performance of different optimization
techniques in the framework of direct methods. Two different classes of evolutionary algorithms, a
conventional genetic algorithm and an estimation of distribution method, are compared in terms of performance
indices statistically evaluated over a prescribed number of runs. At the same time, two different types of problem
representations are considered, a first one based on orbit propagation and a second one based on the solution of
Lambertâs problem for direct transfers. In this way it is possible to highlight how problem representation affects
the capabilities of the considered numerical approaches
Cuckoo Search via Levy Flights
In this paper, we intend to formulate a new metaheuristic algorithm, called
Cuckoo Search (CS), for solving optimization problems. This algorithm is based
on the obligate brood parasitic behaviour of some cuckoo species in combination
with the Levy flight behaviour of some birds and fruit flies. We validate the
proposed algorithm against test functions and then compare its performance with
those of genetic algorithms and particle swarm optimization. Finally, we
discuss the implication of the results and suggestion for further research
Learning joint feature adaptation for zero-shot recognition
Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances. In this paper we propose a novel framework of learning data-dependent feature transforms for scoring similarity between an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly we propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration we design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement. We test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset we can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
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