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

Adaptive Nonparametric Image Parsing

In this paper, we present an adaptive nonparametric solution to the image parsing task, namely annotating each image pixel with its corresponding category label. For a given test image, first, a locality-aware retrieval set is extracted from the training data based on super-pixel matching similarities, which are augmented with feature extraction for better differentiation of local super-pixels. Then, the category of each super-pixel is initialized by the majority vote of the $k$-nearest-neighbor super-pixels in the retrieval set. Instead of fixing $k$ as in traditional non-parametric approaches, here we propose a novel adaptive nonparametric approach which determines the sample-specific k for each test image. In particular, $k$ is adaptively set to be the number of the fewest nearest super-pixels which the images in the retrieval set can use to get the best category prediction. Finally, the initial super-pixel labels are further refined by contextual smoothing. Extensive experiments on challenging datasets demonstrate the superiority of the new solution over other state-of-the-art nonparametric solutions.Comment: 11 page

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

The application of user log for online business environment using content-based Image retrieval system

Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers

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