Fault detection and classification by unsupervised feature extraction and dimensionality reduction
نویسندگان
چکیده
منابع مشابه
Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy
This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Err...
متن کاملFeature 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 کاملDiscriminative Unsupervised Dimensionality Reduction
As an important machine learning topic, dimensionality reduction has been widely studied and utilized in various kinds of areas. A multitude of dimensionality reduction methods have been developed, among which unsupervised dimensionality reduction is more desirable when obtaining label information requires onerous work. However, most previous unsupervised dimensionality reduction methods call f...
متن کاملFeature Sets and Dimensionality Reduction for Visual Object Detection
We describe a family of object detectors that provides state-of-the-art error rates on several important datasets including INRIA people and PASCAL VOC’06 and VOC’07. The method builds on a number of recent advances. It uses the Latent SVM learning framework and a rich visual feature set that incorporates Histogram of Oriented Gradient, Local Binary Pattern and Local Ternary Pattern descriptors...
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: Complex & Intelligent Systems
سال: 2015
ISSN: 2199-4536,2198-6053
DOI: 10.1007/s40747-015-0004-2