Outlier Detection in High Dimensional Data (2021-06-30T00:00:00.000000Z)

TL;DR

The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model for autonomous driving.

Abstract

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

Authors

C. Aggarwal

1 papers

Philip S. Yu

6 papers

Outlier Detection in High Dimensional Data

TL;DR

Abstract

Authors

References10 items

Unsupervised Clustering through Gaussian Mixture Variational AutoEncoder with Non-Reparameterized Variational Inference and Std Annealing

Unsupervised mmWave Beamforming via Autoencoders

Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus

Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders

Unsupervised Anomaly detection of LM Guide Using Variational Autoencoder

Autoencoder Inspired Unsupervised Feature Selection

B) Code: This is the part of the network represents the compressed input that is fed into decoder

Retrieval Number: 100.1/ijeat.E26750610521

A) Encoder

It is an approach for deciding which aspects of observed data are relevant information, compactness of representation measured as the compressibility

Field of Study

Journal Information

Name

Venue Information

Name

Type

URL

Alternate Names