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.

Recovery of corrupted multiple kernels for clustering

Abstract: Kernel-based methods, such as kernel k-means and kernel PCA, have been widely used in machine learning tasks. The performance of these methods critically depends on the selection of kernel functions; however, the challenge is that we usually do not know what kind of kernels is suitable for the given data and task in advance; this leads to research on multiple kernel learning, i.e. we learn a consensus kernel from multiple candidate kernels. Existing multiple kernel learning methods have difficulty in dealing with noises. In this paper, we propose a novel method for learning a robust yet low-rank kernel for clustering tasks. We observe that the noises of each kernel have specific structures, so we can make full use of them to clean multiple input kernels and then aggregate them into a robust, low-rank consensus kernel. The underlying optimization problem is hard to solve and we will show that it can be solved via alternating minimization, whose convergence is theoretically guaranteed. Experimental results on several benchmark data sets further demonstrate the effectiveness of our method.

Online Learning of Structured Predictors with Multiple Kernels

Abstract: Training structured predictors often requires a considerable time selecting features or tweaking the kernel. Multiple kernel learning (MKL) sidesteps this issue by embedding the kernel learning into the training procedure. Despite the recent progress towards efficiency of MKL algorithms, the structured output case remains an open research front. We propose a family of online algorithms able to tackle variants of MKL and group-LASSO, for which we show regret, convergence, and generalization bounds. Experiments on handwriting recognition and dependency parsing attest the success of the approach.

Deep kernel map networks for image annotation

Abstract: Deep multiple kernel learning is a powerful technique that selects and deeply combines multiple elementary kernels in order to provide the best performance on a given classification task. This technique, particularly effective, becomes intractable when handling large scale datasets; indeed, multiple nonlinear kernel combinations are time and memory demanding., In this paper, we propose a new framework that significantly reduces the complexity of deep multiple kernels. Given a deep kernel network (DKN), our method designs its equivalent deep map network (DMN), using multi-layer explicit maps that approximate the initial DKN with a high precision. When combined with support vector machines, the design of DMN preserves high classification accuracy compared to its underlying DKN while being (at least) an order of magnitude faster. Experiments conducted on the challenging Im-ageCLEF2013 annotation benchmark, show that the proposed DMN is indeed effective and highly efficient.

Howis the weather: Automatic inference from images

Abstract: Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance.