Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract aspect-related information. In prior work, attention mechanisms and dependency graph networks are commonly adopted to capture the relations between the context and given aspect. And the weighted sum of context hidden states is used as the final representation fed to the classifier. However, the information related to the given aspect may be already discarded and adverse information may be retained in the context modeling processes of existing models. Such a problem cannot be solved by subsequent modules due to two reasons. First, their operations are conducted on the encoder-generated context hidden states, whose value cannot be changed after the encoder. Second, existing encoders only consider the context while not the given aspect. To address this problem, we argue the given aspect should be considered as a new clue out of context in the context modeling process. As for solutions, we design three streams of aspect-aware context encoders: an aspect-aware LSTM, an aspect-aware GCN, and three aspect-aware BERTs. They are dedicated to generating aspect-aware hidden states which are tailored for the ABSA task. In these aspect-aware context encoders, the semantics of the given aspect is used to regulate the information flow. Consequently, the aspect-related information can be retained and aspect-irrelevant information can be excluded in the generated hidden states. We conduct extensive experiments on several benchmark datasets with empirical analysis, demonstrating the efficacies and advantages of our proposed aspect-aware context encoders.

The number of social images has exploded by the wide adoption of social networks, and people like to share their comments about them. These comments can be a description of the image, or some objects, attributes, scenes in it, which are normally used as the user-provided tags. However, it is well-known that user-provided tags are incomplete and imprecise to some extent. Directly using them can damage the performance of related applications, such as the image annotation and retrieval. In this paper, we propose to learn an image annotation model and refine the user-provided tags simultaneously in a weakly-supervised manner. The deep neural network is utilized as the image feature learning and backbone annotation model, while visual consistency, semantic dependency, and user-error sparsity are introduced as the constraints at the batch level to alleviate the tag noise. Therefore, our model is highly flexible and stable to handle large-scale image sets. Experimental results on two benchmark datasets indicate that our proposed model achieves the best performance compared to the state-of-the-art methods.

Matching pedestrians across multiple camera views has attracted lots of recent research attention due to its apparent importance in surveillance and security applications.While most existing works address this problem in a still-image setting, we consider the more informative and challenging video-based person re-identification problem, where a video of a pedestrian as seen in one camera needs to be matched to a gallery of videos captured by other non-overlapping cameras. We employ a convolutional network to extract the appearance and motion features from raw video sequences, and then feed them into a multi-rate recurrent network to exploit the temporal correlations, and more importantly, to take into account the fact that pedestrians, sometimes even the same pedestrian, move in different speeds across different camera views. The combined network is trained in an end-to-end fashion, and we further propose an initialization strategy via context reconstruction to largely improve the performance. We conduct extensive experiments on the iLIDS-VID and PRID-2011 datasets, and our experimental results confirm the effectiveness and the generalization ability of our model.

The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due to the scarcity of diversity; 2) ISTA does not incorporate the historical information into the updating rules, and the historical information has been proven helpful to speed up the convergence. To address these challenging issues, we propose a novel formulation of ISTA (named as adaptive ISTA) by introducing a novel \textit{adaptive momentum vector}. To efficiently solve the proposed adaptive ISTA, we recast it as a recurrent neural network unit and show its connection with the well-known long short term memory (LSTM) model. With a new proposed unit, we present a neural network (termed SC2Net) to achieve sparse codes in an end-to-end manner. To the best of our knowledge, this is one of the first works to bridge the $\ell_1$-solver and LSTM, and may provide novel insights in understanding model-based optimization and LSTM. Extensive experiments show the effectiveness of our method on both unsupervised and supervised tasks.

Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which aims to find a transformation matrix to convert feature space to a smaller space by maximising the between-class scatter matrix while minimising the within-class scatter matrix. Although the fast and easy process of finding the transformation matrix has made this method attractive, overemphasizing the large class distances makes the criterion of this method suboptimal. In this case, the close class pairs tend to overlap in the subspace. Despite different weighting methods having been developed to overcome this problem, there is still a room to improve this issue. In this work, we study a weighted trace ratio by maximising the harmonic mean of the multiple objective reciprocals. To further improve the performance, we enforce the l 2,1 -norm to the developed objective function. Additionally, we propose an iterative algorithm to optimise this objective function. The proposed method avoids the domination problem of the largest objective, and guarantees that no objectives will be too small. This method can be more beneficial if the number of classes is large. The extensive experiments on different datasets show the effectiveness of our proposed method when compared with four state-of-the-art methods.

The large proportion of irrelevant or noisy features in real-life high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE by using three successive recurrent components to build such sequential ensembles. Given outlier scores output by an existing outlier scoring method on a feature subset, CINFO first defines a Cantelli's inequality-based outlier thresholding function to select outlier candidates with a false positive upper bound. It then performs lasso-based sparse regression by treating the outlier scores as the target feature and the original features as predictors on the outlier candidate set to obtain a feature subset that is tailored for the outlier scoring method. Our experiments show that two different outlier scoring methods enabled by CINFO (i) perform significantly better on 11 real-life high-dimensional data sets, and (ii) have much better resilience to noisy features, compared to their bare versions and three state-of-the-art competitors. The source code of CINFO is available at https://sites.google.com/site/gspangsite/sourcecode.

To reduce the dependence on labeled data, there have been increasing research efforts on learning visual classifiers by exploiting web images. One issue that limits their performance is the problem of polysemy. To solve this problem, in this work, we present a novel framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses to retrieve sense-specific images. Then we merge visual similar semantic senses and prune noises by using the retrieved images. Finally, we train a visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and re-ranking search results demonstrate the superiority of our proposed approach.

Representing the semantics of words is a fundamental task in text processing. Several research studies have shown that text and knowledge bases (KBs) are complementary sources for word embedding learning. Most existing methods only consider relationships within word-pairs in the usage of KBs. We argue that the structural information of well-organized words within the KBs is able to convey more effective and stable knowledge in capturing semantics of words. In this paper, we propose a semantic structure-based word embedding method, and introduce concept convergence and word divergence to reveal semantic structures in the word embedding learning process. To assess the effectiveness of our method, we use WordNet for training and conduct extensive experiments on word similarity, word analogy, text classification and query expansion. The experimental results show that our method outperforms state-of-the-art methods, including the methods trained solely on the corpus, and others trained on the corpus and the KBs.

To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truly relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.

Nonparametric classification models, such as K-Nearest Neighbor (KNN), have become particularly powerful tools in machine learning and data mining, due to their simplicity and flexibility. However, the testing time of the KNN classifier becomes unacceptable and the KNN's performance deteriorates significantly when applied to data sets with millions of dimensions. We observe that state-of-the-art approximate nearest neighbor (ANN) methods aim to either reduce the number of distance comparisons based on tree structure or decrease the cost of distance computation by dimension reduction methods. In this paper, we propose a doubly approximate nearest neighbor classification strategy, which marries the two branches which compress the dimensions for decreasing distance computation cost as well as reduce the number of distance comparison instead of full scan. Under this strategy, we build a compressed dimensional tree (CD-Tree) to avoid unnecessary distance calculations. In each decision node, we propose a novel feature selection paradigm by optimizing the feature selection vector as well as the separator (indicator variables for splitting instances) with the maximum margin. An efficient algorithm is then developed to find the globally optimal solution with convergence guarantee. Furthermore, we also provide a data-dependent generalization error bound for our model, which reveals a new insight for the design of ANN classification algorithms. Our empirical studies show that our algorithm consistently obtains competitive or better classification results on all data sets, yet we can also achieve three orders of magnitude faster than state-of-the-art libraries on very high dimensions.