scholarly journals A Multiscale Clustering Approach for Non-IID Nominal Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Runzi Chen ◽  
Shuliang Zhao ◽  
Zhenzhen Tian
Keyword(s):  
Similarity Measure ◽  
Computational Cost ◽  
Hebei Province ◽  
Single Chain ◽  
Nominal Data ◽  
Real Dataset ◽  
Multiscale Clustering ◽  
Clustering Approach ◽  
Public Datasets ◽  
Research Situation

Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent and identically distributed (Non-IID). Aiming at the current research situation, this paper proposes a multiscale clustering framework based on Non-IID nominal data. Firstly, the benchmark-scale dataset is clustered based on coupled metric similarity measure. Secondly, it is proposed to transform the clustering results from benchmark scale to target scale that the two algorithms are named upscaling based on single chain and downscaling based on Lanczos kernel, respectively. Finally, experiments are performed using five public datasets and one real dataset of the Hebei province of China. The results showed that the method can provide us not only competitive performance but also reduce computational cost.

scholarly journals A Fast Multiscale Clustering Approach Based on DBSCAN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Runzi Chen ◽  
Shuliang Zhao ◽  
Meishe Liang
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Scaling Up ◽  
Practical Significance ◽  
Small Scale ◽  
Large Scale Data ◽  
Traditional Algorithm ◽  
Multiscale Clustering ◽  
Clustering Approach ◽  
Scale Data

Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this paper proposes a multiscale clustering framework based on DBSCAN. This framework uses DBSCAN for clustering small-scale datasets, then introduces algorithm Scaling-Up Cluster Centers (SUCC) generating cluster centers of large-scale datasets by merging clustering results of small-scale datasets, not mining raw large-scale datasets. We show experimentally that, compared to traditional algorithm DBACAN and leading algorithms DBSCAN++ and HDBSCAN, SUCC can provide not only competitive performance but reduce computational cost. In addition, under the guidance of experts, the performance of SUCC is more competitive in accuracy.

scholarly journals A Joint 2D-3D Complementary Network for Stereo Matching

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1430
Author(s):  
Xiaogang Jia ◽  
Wei Chen ◽  
Zhengfa Liang ◽  
Xin Luo ◽  
Mingfei Wu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Research Field ◽  
Disparity Map ◽  
Improve Performance ◽  
Cost Aggregation ◽  
Disparity Range ◽  
Public Datasets ◽  
Coarse To Fine ◽  
Speed And Accuracy

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.

scholarly journals Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Dejun Jiang ◽  
Zhenxing Wu ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Computational Cost ◽  
Large Dataset ◽  
Predictive Capacity ◽  
Classification Tasks ◽  
Graph Neural Networks ◽  
Public Datasets

AbstractGraph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

Y-Net: Dual-branch Joint Network for Semantic Segmentation

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Yizhen Chen ◽  
Haifeng Hu
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Computational Cost ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Global Context ◽  
Multi Level ◽  
The One ◽  
Public Datasets ◽  
High Level

Most existing segmentation networks are built upon a “ U -shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “ U -shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “ U -shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.

scholarly journals Mining of high dimensional data using enhanced clustering approach

2018 ◽  
Vol 7 (2.21) ◽  
pp. 291
Author(s):  
S Sivakumar ◽  
Kumar Narayanan ◽  
Swaraj Paul Chinnaraju ◽  
Senthil Kumar Janahan
Keyword(s):  
Data Mining ◽  
Similarity Measure ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
High Dimensional ◽  
Projected Clustering ◽  
Low Dimensionality ◽  
Clustering Approach ◽  
Numerous Data ◽  
Dimension Space

Extraction of useful data from a set is known as Data mining. Clustering has top information mining process it supposed to help an individual, divide and recognize numerous data from records inside group consistent with positive similarity measure. Clustering excessive dimensional data has been a chief undertaking. Maximum present clustering algorithms have been inefficient if desired similarity is computed among statistics factors inside the complete dimensional space. Varieties of projected clustering algorithms were counseled for addressing those problems. However many of them face problems whilst clusters conceal in some space with low dimensionality. These worrying situations inspire our system to endorse a look at partitional distance primarily based projected clustering set of rules. The aimed paintings is successfully deliberate for projects clusters in excessive huge dimension space via adapting the stepped forward method in k Mediods set of pointers. The main goal for second one gadget is to take away outliers, at the same time as the 1/3 method will find clusters in numerous spaces. The (clustering) technique is based on the adequate Mediods set of guidelines, an excess distance managed to set of attributes everywhere values are dense.

scholarly journals High Performance Gesture Recognition via Effective and Efficient Temporal Modeling

2019 ◽  
Author(s):  
Yang Yi ◽  
Feng Ni ◽  
Yuexin Ma ◽  
Xinge Zhu ◽  
Yuankai Qi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
High Performance ◽  
Short Term Memory ◽  
State Of The Art ◽  
Computational Cost ◽  
Temporal Modeling ◽  
Spatiotemporal Features ◽  
Public Datasets

State-of-the-art hand gesture recognition methods have investigated the spatiotemporal features based on 3D convolutional neural networks (3DCNNs) or convolutional long short-term memory (ConvLSTM). However, they often suffer from the inefficiency due to the high computational complexity of their network structures. In this paper, we focus instead on the 1D convolutional neural networks and propose a simple and efficient architectural unit, Multi-Kernel Temporal Block (MKTB), that models the multi-scale temporal responses by explicitly applying different temporal kernels. Then, we present a Global Refinement Block (GRB), which is an attention module for shaping the global temporal features based on the cross-channel similarity. By incorporating the MKTB and GRB, our architecture can effectively explore the spatiotemporal features within tolerable computational cost. Extensive experiments conducted on public datasets demonstrate that our proposed model achieves the state-of-the-art with higher efficiency. Moreover, the proposed MKTB and GRB are plug-and-play modules and the experiments on other tasks, like video understanding and video-based person re-identification, also display their good performance in efficiency and capability of generalization.

scholarly journals Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models

2020 ◽  
Author(s):  
Dejun Jiang ◽  
Zhenxing Wu ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Computational Cost ◽  
Large Dataset ◽  
Predictive Capacity ◽  
Classification Tasks ◽  
Graph Neural Networks ◽  
Public Datasets

Abstract Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

Sign in / Sign up

Export Citation Format

Share Document