Locality sensitive semi-supervised feature selection

Semi-Supervised Feature Selection with Constraint Sets

In machine learning classification and recognition are crucial tasks. Any object is recognized with the help of features associated with it. Among many features only some leads to classify object correctly. Feature selection is useful technique to detect such specific features. Feature selection is a process of selecting subset of features to reduce number of features (dimensionality reduction)...

Locality sensitive hashing for fast computation of correlational manifold learning based feature space transformations

Manifold learning based techniques have been found to be useful for feature space transformations and semi-supervised learning in speech processing. However, the immense computational requirements in building neighborhood graphs have hindered the application of these techniques to large speech corpora. This paper presents an approach for fast computation of neighborhood graphs in the context of...

Spam, Phishing, and Fraud Detection Using Random Projections, Adversarial Learning, and Semi-Supervised Learning

Estimation of Individual Micro Data from Aggregated Open Data

In this paper, we propose a method of estimating individual micro data from aggregated open data based on semisupervised learning and conditional probability. Firstly, the proposed method collects aggregated open data and support data, which are related to the individual micro data to be estimated. Then, we perform the locality sensitive hashing (LSH) algorithm to find a subset of the support d...

Graph Laplacian for Semi-supervised Feature Selection in Regression Problems

Feature selection is fundamental in many data mining or machine learning applications. Most of the algorithms proposed for this task make the assumption that the data are either supervised or unsupervised, while in practice supervised and unsupervised samples are often simultaneously available. Semi-supervised feature selection is thus needed, and has been studied quite intensively these past f...