scholarly journals Entanglement-Based Feature Extraction by Tensor Network Machine Learning

2021 ◽  
Vol 7 ◽  
Author(s):  
Yuhan Liu ◽  
Wen-Jun Li ◽  
Xiao Zhang ◽  
Maciej Lewenstein ◽  
Gang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Numerical Experiments ◽  
Quantum Information Theory ◽  
Matrix Product ◽  
Class Testing ◽  
Tensor Network ◽  
Extraction Algorithm ◽  
Original Number ◽  
Testing Accuracy

It is a hot topic how entanglement, a quantity from quantum information theory, can assist machine learning. In this work, we implement numerical experiments to classify patterns/images by representing the classifiers as matrix product states (MPS). We show how entanglement can interpret machine learning by characterizing the importance of data and propose a feature extraction algorithm. We show on the MNIST dataset that when reducing the number of the retained pixels to 1/10 of the original number, the decrease of the ten-class testing accuracy is only O (10–3), which significantly improves the efficiency of the MPS machine learning. Our work improves machine learning’s interpretability and efficiency under the MPS representation by using the properties of MPS representing entanglement.

scholarly journals Demographics and Personality Discovery on Social Media: A Machine Learning Approach

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 353
Author(s):  
Sarach Tuomchomtam ◽  
Nuanwan Soonthornphisaj
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Extraction ◽  
Religious Belief ◽  
Residential Area ◽  
Education Level ◽  
Political Affiliation ◽  
Age Group ◽  
Feature Sets ◽  
Extraction Algorithm

This research proposes a new feature extraction algorithm using aggregated user engagements on social media in order to achieve demographics and personality discovery tasks. Our proposed framework can discover seven essential attributes, including gender identity, age group, residential area, education level, political affiliation, religious belief, and personality type. Multiple feature sets are developed, including comment text, community activity, and hybrid features. Various machine learning algorithms are explored, such as support vector machines, random forest, multi-layer perceptron, and naïve Bayes. An empirical analysis is performed on various aspects, including correctness, robustness, training time, and the class imbalance problem. We obtained the highest prediction performance by using our proposed feature extraction algorithm. The result on personality type prediction was 87.18%. For the demographic attribute prediction task, our feature sets also outperformed the baseline at 98.1% for residential area, 94.7% for education level, 92.1% for gender identity, 91.5% for political affiliation, 60.6% for religious belief, and 52.0% for the age group. Moreover, this paper provides the guideline for the choice of classifiers with appropriate feature sets.

scholarly journals Users' Emotions Analysis based on Hybrid Feature Extraction Techniques

2020 ◽  
pp. 291-296
Author(s):  
Sulis Sandiwarno
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Systems ◽  
Experimental Results ◽  
Extraction Techniques ◽  
Machine Learning Classifiers ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
E Learning ◽  
Hybrid Feature Extraction

In order to solve some problems of importance of words and missing relations of semantic between words in the emotional analysis of e-learning systems, the TF-IWF algorithm weighted Word2vec algorithm model was proposed as a feature extraction algorithm. Moreover, to support this study, we employ Multinomial Naïve Bayes (MNB) to obtain more accurate results. There are three mainly steps, firstly, TF-IWF is employed used to compute the weight of word. Second, Word2vec algorithm is adopted to compute the vector of words, Third, we concatenate first and second steps. Finally, the users' opinions data is trained and classified through several machine learning classifiers especially MNB classifier. The experimental results indicate that the proposed method outperformed against previous approaches in terms of precision, recall, F-Score, and accuracy.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Visible Images

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

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