Metrics Space and Norm: Taxonomy to Distance Metrics

نویسندگان

چکیده

A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand data pattern well and make right decision based data. In recent years, studies show that can significantly improve performance or deep model in clustering, classification, recovery tasks, etc. this article, we provide a survey widely used challenges associated with field. The most current conducted area are commonly influenced by Siamese triplet networks utilized associations between samples while employing mutual weights metric (DML). They successful because their ability recognize relationships among similarity. Furthermore, sampling strategy, suitable metric, network structure complex difficult factors for researchers performance. So, article is significant it detailed which these components comprehensively examined valued whole, evidenced assessing numerical findings techniques.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Following Paths in Task Space: Distance Metrics and Planning Algorithms

Many of our everyday jobs we imagine robots accomplishing are defined via a variety of task-specific constraints. In order for robots to perform these tasks, the robot’s motion planners must respect these constraints. While a robotic manipulator moves and plans in its joint or configuration space, many constraints are naturally defined in task space. We focus on the specific constraint asking t...

متن کامل

Distance Metrics and Data Transformation

2 Distance metrics and similarity measures 2 2.1 Distance metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Vector norm and metric . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 The `p norm and `p metric . . . . . . . . . . . . . . . . . . . . . 3 2.4 Distance metric learning . . . . . . . . . . . . . . . . . . . . . . . 6 2.5 The mean as a similarity measure . . . . . . ...

متن کامل

Distance metrics and data transformations

1 Distance metrics and similarity measures 2 1.1 Distance metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Vector norm and metric . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 The `p norm and `p metric . . . . . . . . . . . . . . . . . . . . . 4 1.4 Distance metric learning . . . . . . . . . . . . . . . . . . . . . . . 7 1.5 The mean as a similarity measure . . . . . . ...

متن کامل

Distance Metrics in the Internet

We consider and compare four Internet distance metrics and analyze the predictive power of these metrics in selecting, from a given source, the lowest latency destination from among a candidate set. The four metrics are: IP path length; autonomous system (AS) path length; great circle geographic distance; and previously measured round trip time (RTT). We describe general properties of these fou...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Scientific Programming

سال: 2022

ISSN: ['1058-9244', '1875-919X']

DOI: https://doi.org/10.1155/2022/1911345