We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets.

Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multi-prototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.

Probabilistic Classifiers Chains (PCC) offers interesting properties to solve multi-label classification tasks due to its ability to estimate the joint probability of the labels. However, PCC presents the major drawback of having a high computational cost in the inference process required to predict new samples. Lately, several approaches have been proposed to overcome this issue, including beam search and an epsilon-Approximate algorithm based on uniform-cost search. Surprisingly, the obvious possibility of using heuristic search has not been considered yet. This paper studies this alternative and proposes an admisible heuristic that, applied in combination with A* algorithm, guarantees, not only optimal predictions in terms of subset 0/1 loss, but also that it always explores less nodes than epsilon-Approximate algorithm. In the experiments reported, the number of nodes explored by our method is less than two times the number of labels for all datasets analyzed. But, the difference in explored nodes must be large enough to compensate the overhead of the heuristic in order to improve prediction time. Thus, our proposal may be a good choice for complex multi-label problems.

To address We present a novel negotiation protocol to facilitate this challenge, Alam et al. [2013b] presented a protocol to energy exchange between off-grid homes that facilitate negotiation over energy exchange. Their protocol are equipped with renewable energy generation and restricts the type and number of offers such that negotiation electricity storage. Our protocol imposes restrictions leads to a subgame perfect Nash equilibrium (SPNE). However, over negotiation such that it reduces the complex their protocol only allows point-to-point communication interdependent multi-issue negotiation to one and relies on a fully connected network topology (i.e., where agents have a strategy profile in subgame each home is connected to all other homes in the community) perfect Nash equilibrium. We show that our protocol whereby the number of connections and messages exchanged; is concurrent, scalable and; under certain conditions; grow quadratically with the number of connected leads to Pareto-optimal outcomes.

Feature noising is an effective mechanism on reducing the risk of overfitting. To avoid an explosive searching space, existing work typically assumes that all features share a single noise level, which is often cross-validated. In this paper, we present a Bayesian feature noising model that flexibly allows for dimension-specific or group-specific noise levels, and we derive a learning algorithm that adaptively updates these noise levels. Our adaptive rule is simple and interpretable, by drawing a direct connection to the fitness of each individual feature or feature group. Empirical results on various datasets demonstrate the effectiveness on avoiding extensive tuning and sometimes improving the performance due to its flexibility.

The semantic class (i.e., type) of an entity plays a vital role in many natural language processing tasks, such as question answering. However, most of existing type classification systems extensively rely on hand-crafted features. This paper introduces a hybrid neural model which classifies entity mentions to a wide-coverage set of 22 types derived from DBpedia. It consists of two parts. The mention model uses recurrent neural networks to recursively obtain the vector representation of an entity mention from the words it contains. The context model, on the other hand, employs multilayer perceptrons to obtain the hidden representation for contextual information of a mention. Representations obtained by the two parts are used together to predict the type distribution. Using automatically generated data, these two parts are jointly learned. Experimental studies illustrate that the proposed approach outperforms baseline methods. Moreover, when type information provided by our method is used in a question answering system, we observe a 14.7% relative improvement for the top-1 accuracy of answers.

Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a label-scarce target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To address this challenge, previous work in the literature mainly relies on the large amount of bilingual parallel corpora to bridge the language gap. In many real applications, however, it is often the case that we have some partial parallel data but it is an expensive and time-consuming job to acquire large amount of parallel data on different languages. In this paper, we propose a novel subspace learning framework by leveraging the partial parallel data for cross-lingual sentiment classification. The proposed approach is achieved by jointly learning the document-aligned review data and un-aligned data from the source language and the target language via a non-negative matrix factorization framework. We conduct a set of experiments with cross-lingual sentiment classification tasks on multilingual Amazon product reviews. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.

We study a setting of non-atomic routing in a network of m parallel links with asymmetry of information. While a central entity (such as a GPS navigation system) — a mediator hereafter — knows the cost functions associated with the links, they are unknown to the individual agents controlling the flow. The mediator gives incentive compatible recommendations to agents, trying to minimize the total travel time. Can the mediator do better than when agents minimize their travel time selfishly without coercing agents to follow his recommendations? We study the mediation ratio: the ratio between the mediated equilibrium obtained from an incentive compatible mediation protocol and the social optimum. We find that mediation protocols can reduce the efficiency loss compared to the full revelation alternative, and compared to the non mediated Nash equilibrium. In particular, in the case of two links with affine cost functions, the mediation ratio is at most 8/7, and remains strictly smaller than the price of anarchy of 4/3 for any fixed m. Yet, it approaches the price of anarchy as m grows. For general (monotone) cost functions, the mediation ratio is at most m, a significant improvement over the unbounded price of anarchy

Transfer learning has attracted a lot of attention in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has been proposed to learn more robust or higher-level features for transfer learning. However, to the best of our knowledge, most of the previous approaches neither minimize the difference between domains explicitly nor encode label information in learning the representation. In this paper, we propose a supervised representation learning method based on deep autoencoders for transfer learning. The proposed deep autoencoder consists of two encoding layers: an embedding layer and a label encoding layer. In the embedding layer, the distance in distributions of the embedded instances between the source and target domains is minimized in terms of KL-Divergence. In the label encoding layer, label information of the source domain is encoded using a softmax regression model. Extensive experiments conducted on three real-world image datasets demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

In this paper, we propose a Weakly Supervised Matrix Factorization (WSMF) approach to the problem of image parsing with noisy tags, i.e., segmenting noisily tagged images and then classifying the regions only with image-level labels. Instead of requiring clean but expensive pixel-level labels as strong supervision in the traditional image parsing methods, we take noisy image-level labels as weakly-supervised constraints. Specifically, we first over-segment all the images into multiple regions which are initially labeled based upon the image-level labels. Moreover, from a low-rank matrix factorization viewpoint, we formulate noisily tagged image parsing as a weakly supervised matrix factorization problem. Finally, we develop an efficient algorithm to solve the matrix factorization problem. Experimental results show the promising performance of the proposed WSMF algorithm in comparison with the state-of-the-arts.