Multi-View Diffusion Process for Spectral Clustering and Image Retrieval.

This paper presents a novel approach to multi-view graph learning that combines weight learning and graph learning in an alternating optimization framework. Multi-view graph learning refers to the problem of constructing a unified affinity graph using heterogeneous sources of data representation, which is a popular technique in many learning systems where no prior knowledge of data distribution is available. Our approach is based on a fusion-and-diffusion strategy, in which multiple affinity graphs are fused together via a weight learning scheme based on the unsupervised graph smoothness and utilised as a consensus prior to the diffusion. We propose a novel multi-view diffusion process that learns a manifold-aware affinity graph by propagating affinities on tensor product graphs, leveraging high-order contextual information to enhance pairwise affinities. In contrast to existing multi-view graph learning approaches, our approach is not limited by the quality of initial graphs or the assumption of a latent common subspace among multiple views. Instead, our approach is able to identify the consistency among views and fuse multiple graphs adaptively. We formulate both weight learning and diffusion-based affinity learning in a unified framework and propose an alternating optimization solver that is guaranteed to converge. The proposed approach is applied to image retrieval and clustering tasks on 16 real-world datasets. Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods for both retrieval and clustering on 13 out of 16 datasets.

Invertible Residual Blocks in Deep Learning Networks.

Residual blocks have been widely used in deep learning networks. However, information may be lost in residual blocks due to the relinquishment of information in rectifier linear units (ReLUs). To address this issue, invertible residual networks have been proposed recently but are generally under strict restrictions which limit their applications. In this brief, we investigate the conditions under which a residual block is invertible. A sufficient and necessary condition is presented for the invertibility of residual blocks with one layer of ReLU inside the block. In particular, for widely used residual blocks with convolutions, we show that such residual blocks are invertible under weak conditions if the convolution is implemented with certain zero-padding methods. Inverse algorithms are also proposed, and experiments are conducted to show the effectiveness of the proposed inverse algorithms and prove the correctness of the theoretical results.

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data.

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients' one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.

Semisupervised Learning on Graphs With an Alternating Diffusion Process.

Graph-based semisupervised learning is of great importance in many effective learning systems, particularly in agnostic settings where no parametric information or other prior knowledge about the data distribution is available. It leverages the graph structure to propagate labels from labeled nodes to unlabeled ones. Two separate stages are usually involved: constructing an affinity graph and propagating labels on the graph for transductive inference. It is suboptimal to manage them independently, as the correlation between the affinity graph and the labels would not be fully exploited. In this article, we integrate these two stages into one unified framework by formulating the graph construction as a regularized function estimation problem, similar to label propagation. We then propose an alternating diffusion process to solve them alternately, which allows us to learn the graph and unknown labels in an iterative fashion. With the proposed framework, we can construct a dynamic graph adapted to the given and predicted labels iteratively, resulting in more accurate and robust label propagation performance. Extensive experiments on synthetic data and various real-world data have demonstrated the superiority of the proposed method compared with other state-of-the-art methods.

Automatic Hierarchical Classification of Kelps Using Deep Residual Features.

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, such as kelps. This paper presents an automatic hierarchical classification method local binary classification as opposed to the conventional flat classification to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.

Learning Latent Global Network for Skeleton-based Action Prediction.

Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance.

Response to "Ghost Numbers".

This note clarifies the experimental settings of [1] and shows that the issue raised by [2] is due to a lack of details in [1] which resulted in a misinterpretation of the experimental settings.

Exploiting layerwise convexity of rectifier networks with sign constrained weights.

By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms. We prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights in the second hidden layer and negative weights in the output layer, are capable of separating any number of disjoint pattern sets. Furthermore, the proposed two-hidden-layer SCRNs can decompose the patterns of each class into several clusters so that each cluster is convexly separable from all the patterns from the other classes. This provides a means to learn the pattern structures and analyse the discriminant factors between different classes of patterns. Experimental results are provided to show the benefits of sign constraints in improving classification performance and the efficiency of the proposed MM algorithm.

Learning Clip Representations for Skeleton-Based 3D Action Recognition.

This paper presents a new representation of skeleton sequences for 3D action recognition. Existing methods based on hand-crafted features or recurrent neural networks cannot adequately capture the complex spatial structures and the long-term temporal dynamics of the skeleton sequences, which are very important to recognize the actions. In this paper, we propose to transform each channel of the 3D coordinates of a skeleton sequence into a clip. Each frame of the generated clip represents the temporal information of the entire skeleton sequence and one particular spatial relationship between the skeleton joints. The entire clip incorporates multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We also propose a multitask convolutional neural network (MTCNN) to learn the generated clips for action recognition. The proposed MTCNN processes all the frames of the generated clips in parallel to explore the spatial and temporal information of the skeleton sequences. The proposed method has been extensively tested on six challenging benchmark datasets. Experimental results consistently demonstrate the superiority of the proposed clip representation and the feature learning method for 3D action recognition compared to the existing techniques.

Deep Reconstruction Models for Image Set Classification.

Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods.