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.

Learning to Identify Arabic and German Dialects using Multiple Kernels.

Abstract: We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided only for the Arabic data. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Our approach is shallow and simple, but the empirical results obtained in the shared tasks prove that it achieves very good results. Indeed, we ranked on the first place in the ADI Shared Task with a weighted F1 score of 76.32% (4.62% above the second place) and on the fifth place in the GDI Shared Task with a weighted F1 score of 63.67% (2.57% below the first place).

Event Recognition in Videos by Learning from Heterogeneous Web Sources

Abstract: In this work, we propose to leverage a large number of loosely labeled web videos (e.g., from YouTube) and web images (e.g., from Google/Bing image search) for visual event recognition in consumer videos without requiring any labeled consumer videos. We formulate this task as a new multi-domain adaptation problem with heterogeneous sources, in which the samples from different source domains can be represented by different types of features with different dimensions (e.g., the SIFT features from web images and space-time (ST) features from web videos) while the target domain samples have all types of features. To effectively cope with the heterogeneous sources where some source domains are more relevant to the target domain, we propose a new method called Multi-domain Adaptation with Heterogeneous Sources (MDA-HS) to learn an optimal target classifier, in which we simultaneously seek the optimal weights for different source domains with different types of features as well as infer the labels of unlabeled target domain data based on multiple types of features. We solve our optimization problem by using the cutting-plane algorithm based on group based multiple kernel learning. Comprehensive experiments on two datasets demonstrate the effectiveness of MDA-HS for event recognition in consumer videos.

Combination of Heterogeneous Features for Wrist Pulse Blood Flow Signal Diagnosis via Multiple Kernel Learning

Abstract: Wrist pulse signal is of great importance in the analysis of the health status and pathologic changes of a person. A number of feature extraction methods have been proposed to extract linear and nonlinear, and time and frequency features of wrist pulse signal. These features are heterogeneous in nature and are likely to contain complementary information, which highlights the need for the integration of heterogeneous features for pulse classification and diagnosis. In this paper, we propose a novel effective method to classify the wrist pulse blood flow signals by using the multiple kernel learning (MKL) algorithm to combine multiple types of features. In the proposed method, seven types of features are first extracted from the wrist pulse blood flow signals using the state-of-the-art pulse feature extraction methods, and are then fed to an efficient MKL method, SimpleMKL, to combine heterogeneous features for more effective classification. Experimental results show that the proposed method is promising in integrating multiple types of pulse features to further enhance the classification performance.

3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns

Abstract: In this paper, we propose a fully automatic approach for person-independent 3D facial expression recognition. In order to extract discriminative expression features, each aligned 3D facial surface is compactly represented as multiple global histograms of local normal patterns from multiple normal components and multiple binary encoding scales, namely Multi-Scale Local Normal Patterns (MS-LNPs). 3D facial expression recognition is finally carried out by modeling multiple kernel learning (MKL) to efficiently embed and combine these histogram based features. By using the SimpleMKL algorithm with the chi-square kernel, we achieved an average recognition rate of 80.14% based on a fair experimental setup. To the best of our knowledge, our method outperforms most of the state-of-the-art ones.

Consensus Affinity Graph Learning for Multiple Kernel Clustering

Abstract: Significant attention to multiple kernel graph-based clustering (MKGC) has emerged in recent years, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering However, many existing MKGC methods design a fat model that poses challenges for computational cost and clustering performance, as they learn both an affinity graph and an extra consensus kernel cumbersomely To tackle this challenging problem, this article proposes a new MKGC method to learn a consensus affinity graph directly By using the self-expressiveness graph learning and an adaptive local structure learning term, the local manifold structure of the data in kernel space is preserved for learning multiple candidate affinity graphs from a kernel pool first After that, these candidate affinity graphs are synthesized to learn a consensus affinity graph via a thin autoweighted fusion model, in which a self-tuned Laplacian rank constraint and a top- $k$ neighbors sparse strategy are introduced to improve the quality of the consensus affinity graph for accurate clustering purposes The experimental results on ten benchmark datasets and two synthetic datasets show that the proposed method consistently and significantly outperforms the state-of-the-art methods