Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms

Multi-source data, either from different sensors or disparate features extracted from the same sensor, are valuable for geospatial image analysis due to their potential for providing complementary features. In this paper, a composite-kernel-based feature extraction method is proposed for multi-source remote sensing data classification. Features from different sources are first fused via a weighted composite kernel mapping, and then projected to a lower-dimensional subspace in which kernel local Fisher discriminant analysis (KLFDA) is used to extract the most discriminative information. We hypothesize that after such a projection, multi-source data would have better class separability between classes, and an efficient linear classification model–multinomial logistic regression (MLR) would be suitable for classification. The efficacy of the proposed method is demonstrated via experiments using two different sets of multi-source geospatial data. For feature fusion, the raw spectral data and extended multi-attribute profiles (EMAPs) derived from the hyperspectral image are used as a testbed for multi-source image analysis. The second multi-source testbed used for validation involves sensor fusion, in which the hyperspectral and light detection and ranging (LiDAR) data are utilized. Experimental results show that composite kernel local Fisher’s discriminant analysis when combined with MLR based classifier (CKLFDA-MLR) is very effective at feature extraction and classification of multi-source geospatial images.

Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis

A hierarchical classification method using belief functions

As a highly active topic in computational paralinguistics, speech emotion recognition (SER) aims to explore ideal representations for emotional factors in speech. In order to improve the performance of SER, multiple kernel learning (MKL) dimensionality reduction has been utilized to obtain effective information for recognizing emotions. However, the solution of MKL usually provides only one nonnegative mapping direction for multiple kernels; this may lead to loss of valuable information. To address this issue, we propose a two-dimensional framework for multiple kernel subspace learning. This framework provides more linear combinations on the basis of MKL without nonnegative constraints, which preserves more information in the learning procedures. It also leverages both of MKL and two-dimensional subspace learning, combining them into a unified structure. To apply the framework to SER, we also propose an algorithm, namely generalised multiple kernel discriminant analysis (GMKDA), by employing discriminant embedding graphs in this framework. GMKDA takes advantage of the additional mapping directions for multiple kernels in the proposed framework. In order to evaluate the performance of the proposed algorithm a wide range of experiments is carried out on several key emotional corpora. These experimental results demonstrate that the proposed methods can achieve better performance compared with some conventional and subspace learning methods in dealing with SER.

Ziqiang Wang

Papers

Multiple kernel local Fisher discriminant analysis for face recognition