Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.

Surgical gesture classification from video data

Abstract: Much of the existing work on automatic classification of gestures and skill in robotic surgery is based on kinematic and dynamic cues, such as time to completion, speed, forces, torque, or robot trajectories. In this paper we show that in a typical surgical training setup, video data can be equally discriminative. To that end, we propose and evaluate three approaches to surgical gesture classification from video. In the first one, we model each video clip from each surgical gesture as the output of a linear dynamical system (LDS) and use metrics in the space of LDSs to classify new video clips. In the second one, we use spatio-temporal features extracted from each video clip to learn a dictionary of spatio-temporal words and use a bag-of-features (BoF) approach to classify new video clips. In the third approach, we use multiple kernel learning to combine the LDS and BoF approaches. Our experiments show that methods based on video data perform equally well as the state-of-the-art approaches based on kinematic data.

An overview of kernel alignment and its applications

Abstract: The success of kernel methods is very much dependent on the choice of kernel. Kernel design and learning a kernel from the data require evaluation measures to assess the quality of the kernel. In recent years, the notion of kernel alignment, which measures the degree of agreement between a kernel and a learning task, is widely used for kernel selection due to its effectiveness and low computational complexity. In this paper, we present an overview of the research progress of kernel alignment and its applications. We introduce the basic idea of kernel alignment and its theoretical properties, as well as the extensions and improvements for specific learning problems. The typical applications, including kernel parameter tuning, multiple kernel learning, spectral kernel learning and feature selection and extraction, are reviewed in the context of classification framework. The relationship between kernel alignment and other evaluation measures is also explored. Finally, concluding remarks and future directions are presented.

Multi-label Multiple Kernel Learning

Abstract: We present a multi-label multiple kernel learning (MKL) formulation in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph. We formulate the problem in the kernel-induced feature space and propose to learn the kernel matrix as a linear combination of a given collection of kernel matrices in the MKL framework. The proposed learning formulation leads to a non-smooth min-max problem, which can be cast into a semi-infinite linear program (SILP). We further propose an approximate formulation with a guaranteed error bound which involves an unconstrained convex optimization problem. In addition, we show that the objective function of the approximate formulation is differentiable with Lipschitz continuous gradient, and hence existing methods can be employed to compute the optimal solution efficiently. We apply the proposed formulation to the automated annotation of Drosophila gene expression pattern images, and promising results have been reported in comparison with representative algorithms.

PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features

Abstract: Recently circular RNA (circularRNA) has been discovered as an increasingly important type of long non-coding RNA (lncRNA), playing an important role in gene regulation, such as functioning as miRNA sponges. So it is very promising to identify circularRNA transcripts from de novo assembled transcripts obtained by high-throughput sequencing, such as RNA-seq data. In this study, we presented a machine learning approach, named as PredcircRNA, focused on distinguishing circularRNA from other lncRNAs using multiple kernel learning. Firstly we extracted different sources of discriminative features, including graph features, conservation information and sequence compositions, ALU and tandem repeats, SNP densities and open reading frames (ORFs) from transcripts. Secondly, to better integrate features from different sources, we proposed a computational approach based on a multiple kernel learning framework to fuse those heterogeneous features. Our preliminary 5-fold cross-validation result showed that our proposed method can classify circularRNA from other types of lncRNAs with an accuracy of 0.778, sensitivity of 0.781, specificity of 0.770, precision of 0.784 and MCC of 0.554 in our constructed gold-standard dataset, respectively. Our feature importance analysis based on Random Forest illustrated some discriminative features, such as conservation features and a GTAG sequence motif. Our PredcircRNA tool is available for download at https://github.com/xypan1232/PredcircRNA.

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Abstract: The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched. However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub-industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories. We use financial news articles from multiple categories to predict price movements.The multiple kernel learning approach is proposed for integrating information.Articles are assigned to news categories based on the relevance to the target stock.Simultaneous usage of several news categories improves the forecasting performance.Increasing the number of relevant news categories improves the performance.