Properties of Mean Shift

Yamasaki, Ryoya; Tanaka, Toshiyuki

doi:10.1109/tpami.2019.2913640

Cited by 24 publications

(25 citation statements)

References 75 publications

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“…The MS also can be viewed as a MM-based optimization of a kernel-based objective function. Additionally, the relationship between the MS and the MEM for mode estimation is similar to that of the IRLS and the MEM for the MLR; the MS is more generic [25]. The MEM transforms problems that are easier to directly optimize into harder problems.…”

Section: Proof Of Theoremmentioning

confidence: 97%

Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm

Yamasaki

Tanaka

2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Modal linear regression (MLR) is a method for obtaining a conditional mode predictor as a linear model. We study kernel selection for MLR from two perspectives: "which kernel achieves smaller error?" and "which kernel is computationally efficient?". First, we show that a Biweight kernel is optimal in the sense of minimizing an asymptotic mean squared error of a resulting MLR parameter. This result is derived from our refined analysis of an asymptotic statistical behavior of MLR. Secondly, we provide a kernel class for which iteratively reweighted least-squares algorithm (IRLS) is guaranteed to converge, and especially prove that IRLS with an Epanechnikov kernel terminates in a finite number of iterations. Simulation studies empirically verified that using a Biweight kernel provides good estimation accuracy and that using an Epanechnikov kernel is computationally efficient. Our results improve MLR of which existing studies often stick to a Gaussian kernel and modal EM algorithm specialized for it, by providing guidelines of kernel selection.

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Section: Proof Of Theoremmentioning

confidence: 97%

Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm

Yamasaki

Tanaka

2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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“…TNs group together to form the clusters based on their location for which the k-means clustering [39] is used. In the considered system model, the coverage area of a UAV is a circle with a radius of R that determines the size of a cluster.…”

Section: System Modelmentioning

confidence: 99%

Efficient Task Allocation Protocol for a Hybrid-Hierarchical Spatial-Aerial-Terrestrial Edge-Centric IoT Architecture

Jamalipour

Abkenar

2022

IEICE Trans. Commun.

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In this paper, we propose a novel Hybrid-Hierarchical spatial-aerial-Terrestrial Edge-Centric (H 2 TEC) for the space-air integrated Internet of Things (IoT) networks. (H 2 TEC) comprises unmanned aerial vehicles (UAVs) that act as mobile fog nodes to provide the required services for terminal nodes (TNs) in cooperation with the satellites. TNs in (H 2 TEC) offload their generated tasks to the UAVs for further processing. Due to the limited energy budget of TNs, a novel task allocation protocol, named TOP, is proposed to minimize the energy consumption of TNs while guaranteeing the outage probability and network reliability for which the transmission rate of TNs is optimized. TOP also takes advantage of the energy harvesting by which the low earth orbit satellites transfer energy to the UAVs when the remaining energy of the UAVs is below a predefined threshold. To this end, the harvested power of the UAVs is optimized alongside the corresponding harvesting time so that the UAVs can improve the network throughput via processing more bits. Numerical results reveal that TOP outperforms the baseline method in critical situations that more power is required to process the task. It is also found that even in such situations, the energy harvesting mechanism provided in the TOP yields a more efficient network throughput.

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“…There are various clustering models, such as K-means, mean shift, agglomerative clustering, spectral clustering and etc. The K-means method is one of the most popular and simplest clustering method [20]; the mean shift method is a centroid-based algorithm with the aim of discovering blobs in a smooth density of samples [21]; the spectral clustering method is based on graph theory [22]; the agglomerative clustering method is based on hierarchical methods with the main idea that each observation started in its own cluster, and clusters are successively merged together [23]. Therefore, it is difficult to choose an optimal clustering algorithm to cluster traffic states.…”

Section: Clustering Traffic Statesmentioning

confidence: 99%

Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy

et al. 2021

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Highway system is experiencing increasing traffic congestion with fast-growing number of vehicles in metropolitan areas. Implementing traffic management strategies such as utilizing the hard shoulder as an extra lane could increase highway capacity without extra construction work. This paper presents a method of determining an optimal traffic condition and speed limit of opening hard shoulder. Firstly, the traffic states are clustered using K-Means, mean shift, agglomerative and spectral clustering methods, and the optimal clustering algorithm is selected using indexes including the silhouette score, Davies-Bouldin Index and Caliski-Harabaz Score. The results suggested that the clustering effect of using K-Means method with three categories is optimal. Then, cellular automata model is used to simulate traffic conditions before and after the hard shoulder running strategy is applied. The parameters of the model, including the probabilities of random deceleration, slow start and lane change, are calibrated using real traffic data. Four indicators including the traffic volume, the average speed, the variance of speed, and the travel time of emergency rescue vehicles during traffic accident obtained using the cellular automata model are used to evaluate various hard shoulder running strategies. By using factor analysis and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods, the optimal traffic condition and speed limit of opening hard shoulder could be determined. This method could be applied to highway segments of various number of lanes and different speed limits to optimize the hard shoulder running strategy for highway management.

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Properties of Mean Shift

Cited by 24 publications

References 75 publications

Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm

Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm

Efficient Task Allocation Protocol for a Hybrid-Hierarchical Spatial-Aerial-Terrestrial Edge-Centric IoT Architecture

Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy

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