Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Parallel Unsupervised Feature Learning with Sparse AutoEncoder

Choosing the right input features for a particular machine learning algorithm is one of the deciding factors for a successful application but often a time consuming manual task. Unsupervised feature learning algorithms provide an alternative solution by automatically learning features; however, they are usually computationally intensive. In this project we explore different implementations of t...

Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

Unsupervised feature selection for sparse data

Feature selection is a well-known problem in machine learning and pattern recognition. Many high-dimensional datasets are sparse, that is, many features have zero value. In some cases, we do not known the class label for some (or even all) patterns in the dataset, leading us to semi-supervised or unsupervised learning problems. For instance, in text classification with the bag-of-words (BoW) re...

Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

Feature Selection in Data Mining