Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

Machine Learning and Pattern Recognition Principal Component Analysis

10-701 Machine Learning (Spring 2012) Principal Component Analysis

One could think of many reasons where transforming a data set at hand to a lowdimensional space might be desirable, e.g. it makes the data easier to manipulate with and requires less computational resource. It is, however, important to perform such transformations in a principled way because any kind of dimension reduction might lead to loss of information, and it is crucial that the algorithm ...

10-601 Machine Learning (Fall 2010) Principal Component Analysis

One could think of many reasons where transforming a data set at hand to a lowdimensional space might be desirable, e.g. it makes the data easier to manipulate with and requires less computational resource. It is, however, important to perform such transformations in a principled way because any kind of dimension reduction might lead to loss of information, and it is crucial that the algorithm ...

Compression of Breast Cancer Images By Principal Component Analysis

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...

Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains

In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...