Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. Most existing works focus on modeling user-item interactions only from the item level, ignoring that they are driven by latent shopping intentions (e.g., ballpoint pens, miniatures, etc). The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences. Despite its significance, the area of main shopping intention detection remains under-investigated in the academic literature. To fill this gap, we propose a graph-regularized stochastic Transformer method, G-STO. By considering intentions as sets of products and user preferences as compositions of intentions, we model both of them as stochastic Gaussian embeddings in the latent representation space. Instead of training the stochastic representations from scratch, we develop a global intention relational graph as prior knowledge for regularization, allowing relevant shopping intentions to be distributionally close. Finally, we feed the newly regularized stochastic embeddings into Transformer-based models to encode sequential information from the intention transitions. We evaluate our main shopping intention identification model on three different real-world datasets, where G-STO achieves significantly superior performances to the baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10 on average.

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection. Autoencoder is a popular mechanism to accomplish the goal of dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples. Inspired by the success of neighborhood aware shortest path based geodesic approximatiors such as ISOMAP, in this work, we propose to use a minimum spanning tree (MST), a graph-based algorithm, to approximate the local neighborhood structure and generate structure-preserving distances among data points. We use this MST-based distance metric to replace the Euclidean distance metric in the embedding function of autoencoders and develop a new graph regularized autoencoder, which outperforms, over 20 benchmark anomaly detection datasets, the plain autoencoder using no regularizer as well as the autoencoders using the Euclidean-based regularizer. We furthermore incorporate the MST regularizer into two generative adversarial networks and find that using the MST regularizer improves the performance of anomaly detection substantially for both generative adversarial networks.

This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views for clustering and are unable to fully exploit the valuable information. Therefore, we propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC), which simultaneously integrates a consistent graph regularizer with a complementary graph regularizer into the objective function. In particular, the consistent graph regularizer learns the intrinsic affinity relationship of data points shared by all views. The complementary graph regularizer investigates the specific information of multiple views. It is noteworthy that the consistent and complementary regularizers are formulated by two different graphs constructed from the first-order proximity and second-order proximity of multiple views, respectively. The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering. Extensive experiments on six benchmark datasets serve to validate the effectiveness of the proposed method over other state-of-the-art multi-view clustering methods.

Person re-identification (re-ID) is a fundamental task in automated video surveillance. In real-world visual surveillance systems, a person is often captured in quite low resolutions. So we often need to perform low-resolution person re-ID, where images captured by different cameras have great resolution divergences. Existing methods cope problem via some complicated and time-consuming strategies, making them less favorable in practice, and their performances are far from satisfactory. In this paper, we design a novel Discriminative Semi-coupled Projective Dictionary Learning (DSPDL) model to effectively and efficiently solve this problem. Specifically, we propose to jointly learn a pair of dictionaries and a mapping to bridge the gap across low(er) and high(er) resolution person images. Besides, we develop a novel graph regularizer to incorporate positive and negative image pair information in a parameterless fashion. Meanwhile, we adopt the efficient and powerful projective dictionary learning technique to boost the our efficiency. Experiments on three public datasets show the superiority of the proposed method to the state-of-the-art ones.

We describe a graph-based semi-supervised learning framework in the context of deep neural networks that uses a graph-based entropic regularizer to favor smooth solutions over a graph induced by the data. The main contribution of this work is a computationally efficient, stochastic graph-regularization technique that uses mini-batches that are consistent with the graph structure, but also provides enough stochasticity (in terms of mini-batch data diversity) for convergence of stochastic gradient descent methods to good solutions. For this work, we focus on results of frame-level phone classification accuracy on the TIMIT speech corpus but our method is general and scalable to much larger data sets. Results indicate that our method significantly improves classification accuracy compared to the fully-supervised case when the fraction of labeled data is low, and it is competitive with other methods in the fully labeled case.

We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting. We utilize a technique, first described in [13] for the construction of mini-batches for stochastic gradient descent (SGD) based on synthesized partitions of an affinity graph that are consistent with the graph structure, but also preserve enough stochasticity for convergence of SGD to good local minima. We show how our technique allows a graph-based semi-supervised loss function to be decomposed into a sum over objectives, facilitating data parallelism for scalable training of machine learning models. Empirical results indicate that our method significantly improves classification accuracy compared to the fully-supervised case when the fraction of labeled data is low, and in the parallel case, achieves significant speed-up in terms of wall-clock time to convergence. We show the results for both sequential and distributed-memory semi-supervised DNN training on a speech corpus.

Multi-view data is highly common nowadays, since various view-points and different sensors tend to facilitate better data representation. However, data from different views show a large divergence. Specifically, one sample lies in two kinds of structures, one is class structure and the other is view structure, which are intertwined with one another in the original feature space. To address this, we develop a Robust Multi-view Subspace Learning algorithm (RMSL) through dual low-rank decompositions, which desires to seek a low-dimensional view-invariant subspace for multi-view data. Through dual low-rank decompositions, RMSL aims to disassemble two intertwined structures from each other in the low-dimensional subspace. Furthermore, we develop two novel graph regularizers to guide dual low-rank decompositions in a supervised fashion. In this way, the semantic gap across different views would be mitigated so that RMSL can preserve more within-class information and reduce the influence of view variance to seek a more robust low-dimensional subspace. Extensive experiments on two multi-view benchmarks, e.g., face and object images, have witnessed the superiority of our proposed algorithm, by comparing it with the state-of-the-art algorithms.