A new multi-view learning machine with incomplete data

Multi-view learning with enhanced multi-weight vector projection support vector machine

Multi-view learning aims on learning from the data represented by multiple distinct feature sets. Various multi-view support vector machine methods have been successfully applied to classification tasks. However, the existed methods often face the problems of long processing time or weak generalization on some complex datasets. In this paper, two multi-view enhanced multi-weight vector projection support vector machine models are proposed. One is a ratio form of multi-view enhanced multi-weight vector projection support vector machine (R-MvEMV), while the other is a difference form (D-MvEMV). Instead of searching for specific classification hyperplanes, each proposed model tries to generate two projection matrices composed of a set of projection vectors for each view. A co-regularization term is added to maximize the consistency of different views. R-MvEMV and D-MvEMV can be simplified to two generalized eigenvalue problems and two eigenvalue problems, respectively. The optimal weight vector projections are the eigenvectors corresponding to the smallest eigenvalues. Some numerical tests are conducted to compare the proposed methods with the other state-of-art multi-view support vector machine methods. The numerical results show the better classification performance and higher efficiency of the proposed methods.

A survey on missing data in machine learning

Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.