The hierarchical method is an unsupervised method: the analysis of the data is done without any a priori on the number of classes expected, but a major weakness of the hierarchical algorithm is that an improper fusion of data at an early stage cannot be corrected later.
A multiclass support vector machine (MC-SVM) method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers.
The results obtained highlight the performance of our method as an unsupervised and computationally inexpensive domain partitioning tool.
This method applied an unsupervised clustering to the feature vectors from all the different classes, and then used a supervised labeling method to select two types of clusters: discriminant and common clusters.
In this paper, we propose a novel spatial pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R2FP), to better extract rich and robust representation from sparse feature maps learned from the raw data.
Topic 17 shows that our method in an unsupervised way analyzes an input data set from many different angles.
Kernel density estimation, also known as the Parzen window method, is an unsupervised and non-parametric technique for estimating a continuous probability density function from sample data [ 15, 16].
In this study, we used k-means clustering method that is an unsupervised learning method known to solve clustering problems.
The main difference between PCA and LDA is that LDA is a supervised method and PCA is an unsupervised method.
1. Hierarchical clustering method: Hierarchical clustering is an unsupervised method of grouping list of items through successive merging based on similarity or successive division based on dissimilarity.
