Large-Scale Local Online Similarity/Distance Learning Framework Based on Passive/Aggressive

Similarity/distance measures play a key role in many machine learning, pattern recognition, and data mining algorithms, which leads to the emergence of the metric learning field. Many metric learning algorithms learn a global distance function from data that satisfies the constraints of the problem. However, in many real-world datasets, where the discrimination power of features varies in the different regions of input space, a global metric is often unable to capture the complexity of the task. To address this challenge, local metric learning methods are proposed which learn multiple metrics across the different regions of the input space. Some advantages of these methods include high flexibility and learning a nonlinear mapping, but they typically achieve at the expense of higher time requirements and overfitting problems. To overcome these challenges, this research presents an online multiple metric learning framework. Each metric in the proposed framework is composed of a global and a local component learned simultaneously. Adding a global component to a local metric efficiently reduces the problem of overfitting. The proposed framework is also scalable with both sample size and the dimension of input data. To the best of our knowledge, this is the first local online similarity/distance learning framework based on Passive/Aggressive (PA). In addition, for scalability with the dimension of input data, Dual Random Projection (DRP) is extended for local online learning in the present work. It enables our methods to run efficiently on high-dimensional datasets while maintaining their predictive performance. The proposed framework provides a straightforward local extension to any global online similarity/distance learning algorithm based on PA. Experimental results on some challenging datasets from machine vision community confirm that the extended methods considerably enhance the performance of the related global ones without increasing the time complexity.

Similarity-Based Virtual Screen Using Enhanced Siamese Multi-Layer Perceptron

Traditional drug development is a slow and costly process that leads to the production of new drugs. Virtual screening (VS) is a computational procedure that measures the similarity of molecules as one of its primary tasks. Many techniques for capturing the biological similarity between a test compound and a known target ligand have been established in ligand-based virtual screens (LBVSs). However, despite the good performances of the above methods compared to their predecessors, especially when dealing with molecules that have structurally homogenous active elements, they are not satisfied when dealing with molecules that are structurally heterogeneous. The main aim of this study is to improve the performance of similarity searching, especially with molecules that are structurally heterogeneous. The Siamese network will be used due to its capability to deal with complicated data samples in many fields. The Siamese multi-layer perceptron architecture will be enhanced by using two similarity distance layers with one fused layer, then multiple layers will be added after the fusion layer, and then the nodes of the model that contribute less or nothing during inference according to their signal-to-noise ratio values will be pruned. Several benchmark datasets will be used, which are: the MDL Drug Data Report (MDDR-DS1, MDDR-DS2, and MDDR-DS3), the Maximum Unbiased Validation (MUV), and the Directory of Useful Decoys (DUD). The results show the outperformance of the proposed method on standard Tanimoto coefficient (TAN) and other methods. Additionally, it is possible to reduce the number of nodes in the Siamese multilayer perceptron model while still keeping the effectiveness of recall on the same level.

Reliable Memory Model for Visual Tracking

Effectively learning the appearance change of a target is the key point of an online tracker. When occlusion and misalignment occur, the tracking results usually contain a great amount of background information, which heavily affects the ability of a tracker to distinguish between targets and backgrounds, eventually leading to tracking failure. To solve this problem, we propose a simple and robust reliable memory model. In particular, an adaptive evaluation strategy (AES) is proposed to assess the reliability of tracking results. AES combines the confidence of the tracker predictions and the similarity distance, which is between the current predicted result and the existing tracking results. Based on the reliable results of AES selection, we designed an active–frozen memory model to store reliable results. Training samples stored in active memory are used to update the tracker, while frozen memory temporarily stores inactive samples. The active–frozen memory model maintains the diversity of samples while satisfying the limitation of storage. We performed comprehensive experiments on five benchmarks: OTB-2013, OTB-2015, UAV123, Temple-color-128, and VOT2016. The experimental results show that our tracker achieves state-of-the-art performance.

New Network Selection Algorithm Based on Cosine Similarity Distance and PSO in Heterogeneous Wireless Networks

Future wireless communication networks will be composed of different technologies with complementary characteristics. Thus, vertical handover (VHO) must support seamless mobility in such heterogeneous environments. The network selection is an important phase in the VHO process and it can be formulated as a multiattribute decision-making problem. So, the mobile terminal equipped with multiple interfaces will be able to choose the most suitable network. This work proposes an access network selection algorithm, based on cosine similarity distance, subjective weights using Fuzzy ANP, and objective weights using particle swarm optimization. The comprehensive weights are based on the cosine similarity distance between the networks and the ideal network. Finally, the candidate network with the minimum cosine distance to the ideal network will be selected in the VHO network selection stage. The performance analysis shows that our proposed method, based on cosine similarity distance and combination weights, reduces the ranking abnormality and number of handoffs in comparison with other MADM methods in the literature.

Near Duplicate Image Detection Using Image Net Model

The rapid development in the technology of Internet and the increase in the usage of mobile devices, it is very easy for users to capture, communicate and share the images through the networks. The spectacular achievement of convolution neural networks in the area of computer vision, will help us to match the features that are very similar between the images for detecting the duplicate version of the image. In this project we use Image Net model that mainly provide a large database that contains many images of different categories. Flask framework is used in this project, which includes many libraries, modules that helps the web developer to write web application. In this project the user is allowed to upload the image, then the image features will be extracted and fed to the CNN model. The CNN model will calculate the similarity distance between the images that is already present in the database and detect the top four images that are duplicate version of the uploaded image.

k-Nearest Neighbour Classifiers - A Tutorial

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data. This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.

Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study is about large-scale social restrictions (PSBB). This study aims to classify negative and positive sentiments by applying the K-Nearest Neighbor algorithm to see the accuracy value of 3 types of distance calculation which are cosine similarity, euclidean, and manhattan distance for Indonesian language tweets about large-scale social restrictions (PSBB) from social media twitter. With the results obtained, the K-Nearest Neighbor accuracy by the Cosine Similarity distance 82% at k = 3, K-Nearest Neighbor by the Euclidean Distance with an accuracy of 81% at k = 11 and K-Nearest Neighbor by Manhattan Distance with an accuracy 80% at k = 5, 7, 9, 11, and 13. So, in this study the K-Nearest Neighbor algorithm with the Cosine Similarity Distance calculation gets the highest point.