Binary Class Labels Research Articles

Tensor-based projection using ridge regression and its application to action classification

In this study, the authors encode video sequence by a third-order tensor instead of matrices or vectors and present a novel framework of action classification via tensor-based projection using ridge regression (TPRR). The main contributions of this framework include: (i) Classification can be directly achieved by projecting the tensor samples into their binary class label through ridge regression technique. (ii) TPRR algorithm is further generalised from binary classification to solve multi-category classification problem by incorporating the error correcting concept. Experiments of action classification on a small or medium training set demonstrate that this approach outperforms previous tensor embedding methods and other state-of-the-art techniques, whereas on large training set, it is also able to achieve very satisfactory results. The proposed approach is further proven to be considerably robust to viewpoint, partial occlusion and irregularities in motion styles.

Supervised multidimensional scaling for visualization, classification, and bipartite ranking

Least squares multidimensional scaling (MDS) is a classical method for representing a n × n dissimilarity matrix D . One seeks a set of configuration points z 1 , … , z n ∈ R S such that D is well approximated by the Euclidean distances between the configuration points: D i j ≈ ‖ z i − z j ‖ 2 . Suppose that in addition to D , a vector of associated binary class labels y ∈ { 1 , 2 } n corresponding to the n observations is available. We propose an extension to MDS that incorporates this outcome vector. Our proposal, supervised multidimensional scaling (SMDS), seeks a set of configuration points z 1 , … , z n ∈ R S such that D i j ≈ ‖ z i − z j ‖ 2 , and such that z i s > z j s for s = 1 , … , S tends to occur when y i > y j . This results in a new way to visualize the observations. In addition, we show that SMDS leads to a method for the classification of test observations, which can also be interpreted as a solution to the bipartite ranking problem. This method is explored in a simulation study, as well as on a prostate cancer gene expression data set and on a handwritten digits data set.

Bayesian modelling of shared gene function

Biological assays are often carried out on tissues that contain many cell lineages and active pathways. Microarray data produced using such material therefore reflect superimpositions of biological processes. Analysing such data for shared gene function by means of well-matched assays may help to provide a better focus on specific cell types and processes. The identification of genes that behave similarly in different biological systems also has the potential to reveal new insights into preserved biological mechanisms. In this article, we propose a hierarchical Bayesian model allowing integrated analysis of several microarray data sets for shared gene function. Each gene is associated with an indicator variable that selects whether binary class labels are predicted from expression values or by a classifier which is common to all genes. Each indicator selects the component models for all involved data sets simultaneously. A quantitative measure of shared gene function is obtained by inferring a probability measure over these indicators. Through experiments on synthetic data, we illustrate potential advantages of this Bayesian approach over a standard method. A shared analysis of matched microarray experiments covering (a) a cycle of mouse mammary gland development and (b) the process of in vitro endothelial cell apoptosis is proposed as a biological gold standard. Several useful sanity checks are introduced during data analysis, and we confirm the prior biological belief that shared apoptosis events occur in both systems. We conclude that a Bayesian analysis for shared gene function has the potential to reveal new biological insights, unobtainable by other means. An online supplement and MatLab code are available at http://www.sykacek.net/research.html#mcabf

Feature extraction based on ica for binary classification problems

In manipulating data such as in supervised learning, we often extract new features from the original features for the purpose of reducing the dimensions of feature space and achieving better performance. In this paper, we show how standard algorithms for independent component analysis (ICA) can be appended with binary class labels to produce a number of features that do not carry information about the class labels-these features will be discarded-and a number of features that do. We also provide a local stability analysis of the proposed algorithm. The advantage is that general ICA algorithms become available to a task of feature extraction for classification problems by maximizing the joint mutual information between class labels and new features, although only for two-class problems. Using the new features, we can greatly reduce the dimension of feature space without degrading the performance of classifying systems.