ترغب بنشر مسار تعليمي؟ اضغط هنا

Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means

129   0   0.0 ( 0 )
 نشر من قبل Yuting Ng
 تاريخ النشر 2020
والبحث باللغة English
 تأليف Yuting Ng




اسأل ChatGPT حول البحث

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption. We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%.



قيم البحث

اقرأ أيضاً

$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of the Gaussian distribution, $k$-means algorithm suffers from relatively poor performance on the dataset containing heavy-tailed data or outliers. Besides, standard $k$-means algorithm also has relatively weak stability, $i.e.$ its results have a large variance, which reduces its credibility. In this paper, we propose a robust and stable $k$-means variant, dubbed the $t$-$k$-means, as well as its fast version to alleviate those problems. Theoretically, we derive the $t$-$k$-means and analyze its robustness and stability from the aspect of the loss function and the expression of the clustering center, respectively. Extensive experiments are also conducted, which verify the effectiveness and efficiency of the proposed method. The code for reproducing main results is available at url{https://github.com/THUYimingLi/t-k-means}.
89 - Carlo Baldassi 2019
We present a simple heuristic algorithm for efficiently optimizing the notoriously hard minimum sum-of-squares clustering problem, usually addressed by the classical k-means heuristic and its variants. The algorithm, called recombinator-k-means, is v ery similar to a genetic algorithmic scheme: it uses populations of configurations, that are optimized independently in parallel and then recombined in a next-iteration population batch by exploiting a variant of the k-means++ seeding algorithm. An additional reweighting mechanism ensures that the population eventually coalesces into a single solution. Extensive tests measuring optimization objective vs computational time on synthetic and real-word data show that it is the only choice, among state-of-the-art alternatives (simple restarts, random swap, genetic algorithm with pairwise-nearest-neighbor crossover), that consistently produces good results at all time scales, outperforming competitors on large and complicated datasets. The only parameter that requires tuning is the population size. The scheme is rather general (it could be applied even to k-medians or k-medoids, for example). Our implementation is publicly available at https://github.com/carlobaldassi/RecombinatorKMeans.jl.
101 - Nicolas Fraiman , Zichao Li 2020
Biclustering is the task of simultaneously clustering the rows and columns of the data matrix into different subgroups such that the rows and columns within a subgroup exhibit similar patterns. In this paper, we consider the case of producing block-d iagonal biclusters. We provide a new formulation of the biclustering problem based on the idea of minimizing the empirical clustering risk. We develop and prove a consistency result with respect to the empirical clustering risk. Since the optimization problem is combinatorial in nature, finding the global minimum is computationally intractable. In light of this fact, we propose a simple and novel algorithm that finds a local minimum by alternating the use of an adapted version of the k-means clustering algorithm between columns and rows. We evaluate and compare the performance of our algorithm to other related biclustering methods on both simulated data and real-world gene expression data sets. The results demonstrate that our algorithm is able to detect meaningful structures in the data and outperform other competing biclustering methods in various settings and situations.
101 - Yiwei Li 2019
This article briefly introduced Arthur and Vassilvitshiis work on textbf{k-means++} algorithm and further generalized the center initialization process. It is found that choosing the most distant sample point from the nearest center as new center can mostly have the same effect as the center initialization process in the textbf{k-means++} algorithm.
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-means to outperform traditional two-step feature extraction and shallow-c lustering strategies. We achieve this by developing a gradient-estimator for the non-differentiable k-means objective via the Gumbel-Softmax reparameterisation trick. In contrast to previous attempts at deep clustering, our concrete k-means model can be optimised with respect to the canonical k-means objective and is easily trained end-to-end without resorting to alternating optimisation. We demonstrate the efficacy of our method on standard clustering benchmarks.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا