Neural Class-Specific Regression for face verification

Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques. While it has been shown that kernel-based Class-Specific Discriminant Analysis is able to provide excellent performance in small- and medium-scale face verification problems, its application in today's large-scale problems is difficult due to its training space and computational requirements. In this paper, generalizing our previous work on kernel-based class-specific discriminant analysis, we show that class-specific subspace learning can be cast as a regression problem. This allows us to derive linear, (reduced) kernel and neural network-based class-specific discriminant analysis methods using efficient batch and/or iterative training schemes, suited for large-scale learning problems. We test the performance of these methods in two datasets describing medium- and large-scale face verification problems.Comment: 9 pages, 4 figure

Multimodal Subspace Support Vector Data Description

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary material (27 tables, 10 figures). The manuscript and supplementary material are combined as a single .pdf (50 pages) fil

Deep Multi-view Learning to Rank

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e. university ranking, multi-view lingual text ranking and image data ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD

Multi-view Data Analysis

Multi-view data analysis is a key technology for making effective decisions by leveraging information from multiple data sources. The process of data acquisition across various sensory modalities gives rise to the heterogeneous property of data. In my thesis, multi-view data representations are studied towards exploiting the enriched information encoded in different domains or feature types, and novel algorithms are formulated to enhance feature discriminability. Extracting informative data representation is a critical step in visual recognition and data mining tasks. Multi-view embeddings provide a new way of representation learning to bridge the semantic gap between the low-level observations and high-level human comprehensible knowledge benefitting from enriched information in multiple modalities.Recent advances on multi-view learning have introduced a new paradigm in jointly modeling cross-modal data. Subspace learning method, which extracts compact features by exploiting a common latent space and fuses multi-view information, has emerged proiminent among different categories of multi-view learning techniques. This thesis provides novel solutions in learning compact and discriminative multi-view data representations by exploiting the data structures in low dimensional subspace. We also demonstrate the performance of the learned representation scheme on a number of challenging tasks in recognition, retrieval and ranking problems.The major contribution of the thesis is a unified solution for subspace learning methods, which is extensible for multiple views, supervised learning, and non-linear transformations. Traditional statistical learning techniques including Canonical Correlation Analysis, Partial Least Square regression and Linear Discriminant Analysis are studied by constructing graphs of specific forms under the same framework. Methods using non-linear transforms based on kernels and (deep) neural networks are derived, which lead to superior performance compared to the linear ones. A novel multi-view discriminant embedding method is proposed by taking the view difference into consideration. Secondly, a multiview nonparametric discriminant analysis method is introduced by exploiting the class boundary structure and discrepancy information of the available views. This allows for multiple projecion directions, by relaxing the Gaussian distribution assumption of related methods. Thirdly, we propose a composite ranking method by keeping a close correlation with the individual rankings for optimal rank fusion. We propose a multi-objective solution to ranking problems by capturing inter-view and intra-view information using autoencoderlike networks. Finally, a novel end-to-end solution is introduced to enhance joint ranking with minimum view-specific ranking loss, so that we can achieve the maximum global view agreements within a single optimization process.In summary, this thesis aims to address the challenges in representing multi-view data across different tasks. The proposed solutions have shown superior performance in numerous tasks, including object recognition, cross-modal image retrieval, face recognition and object ranking

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches