Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis
نویسندگان
چکیده
منابع مشابه
Feature extraction and dimensionality reduction for mass spectrometry data
Mass spectrometry is being used to generate protein profiles from human serum, and proteomic data obtained from mass spectrometry have attracted great interest for the detection of early stage cancer. However, high dimensional mass spectrometry data cause considerable challenges. In this paper we propose a feature extraction algorithm based on wavelet analysis for high dimensional mass spectrom...
متن کاملDimensionality reduction techniques for multivariate data classification, interactive visualization, and analysis-systematic feature selection vs. extraction
The curse of dimensionality, i.e., the fact that feature spaces of increasing dimensionality with finite sample sizes tend to be empty, has given incentive to a plethora of research activities in various disciplines and diverse application fields, e.g., statistics or neural networks. Three major application fields are multivariate data classification, data analysis, and data visualization. In t...
متن کاملFeature Extraction and Efficiency Comparison Using Dimension Reduction Methods in Sentiment Analysis Context
Nowadays, users can share their ideas and opinions with widespread access to the Internet and especially social networks. On the other hand, the analysis of people's feelings and ideas can play a significant role in the decision making of organizations and producers. Hence, sentiment analysis or opinion mining is an important field in natural language processing. One of the most common ways to ...
متن کاملA New Method for Performance Analysis in Nonlinear Dimensionality Reduction
In this paper, we develop a local rank correlation measure which quantifies the performance of dimension reduction methods. The local rank correlation is easily interpretable, and robust against the extreme skewness of nearest neighbor distributions in high dimensions. Some benchmark datasets are studied. We find that the local rank correlation closely corresponds to our visual interpretation o...
متن کاملA Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters
Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...
متن کاملذخیره 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: Sensors
سال: 2020
ISSN: 1424-8220
DOI: 10.3390/s20236944