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.

Sparse Multiple Kernel Learning for Hyperspectral Image Classification Using Spatial-Spectral Features

Abstract: The increase in spatial and spectral resolution of the satellite sensors has provided high-quality data for remote sensing image classification. However, the high-dimensional feature space induced by using many spatial information precludes the use of simple classifiers. This paper proposes to classify the hyperspectral images and simultaneously to learn significant features in such high-dimensional scenarios. Group lasso regularized multiple kernel learning (GLMKL) is used to incorporate extended multi-attribute profile (EMAP) for hyperspectral image classification. We formulate a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL, and the derived variant equivalence leads to an efficient algorithm for MKL. Experiments are conducted on three high spatial resolution hyperspectral data sets. The results show that the proposed method achieves better performance for hyperspectral image classification compared to several state-of-the-art algorithms.

Optimality Implies Kernel Sum Classifiers are Statistically Efficient

Abstract: We propose a novel combination of optimization tools with learning theory bounds in order to analyze the sample complexity of optimal kernel sum classifiers. This contrasts the typical learning theoretic results which hold for all (potentially suboptimal) classifiers. Our work also justifies assumptions made in prior work on multiple kernel learning. As a byproduct of our analysis, we also provide a new form of Rademacher complexity for hypothesis classes containing only optimal classifiers.

PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning

Abstract: BACKGROUND Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. RESULTS In this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. CONCLUSIONS PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.

Saliency prediction with scene structural guidance

Abstract: Previous works have suggested the role of scene information in directing gaze. The structure of a scene provides global contextual information that complements local object information in saliency prediction. In this study, we explore how scene envelopes such as openness, depth, and perspective affect visual attention in natural outdoor images. To facilitate this study, an eye tracking dataset is first built with 500 natural scene images and eye tracking data with 15 subjects free-viewing the images. We make observations on scene layout properties and propose a set of scene structural features relating to visual attention. We further integrate features from deep neural networks and use the set of complementary features for saliency prediction. Our features are independent of and can work together with many computational modules, and this work demonstrates the use of Multiple kernel learning (MKL) as an example to integrate the features at low- and high-levels. Experimental results demonstrate that our model outperforms existing methods and our scene structural features can improve the performance of other saliency models in outdoor scenes.