Utilizing multiple descriptions/views of an object is often useful in image clustering tasks. Despite many works that have been proposed to effectively cluster multi-view data, there are still unaddressed problems such as the errors introduced by the traditional spectral-based clustering methods due to the two disjoint stages: 1) eigendecomposition and 2) the discretization of new representations. In this paper, we propose a unified clustering framework which jointly learns the two stages together as well as utilizing multiple descriptions of the data. More specifically, two learning methods from this framework are proposed: 1) through a graph construction from different views and 2) through combining multiple graphs. Furthermore, benefiting from the separability and local graph preserving properties of the proposed methods, a novel unsupervised automatic attribute discovery method is proposed. We validate the efficacy of our methods on five data sets, showing that the proposed joint learning clustering methods outperform the recent state-of-the-art methods. We also show that it is possible to derive a novel method to address the unsupervised automatic attribute discovery tasks.

It is our great pleasure to introduce you to the technical program of the 22 nd International Conference on Pattern Recognition (ICPR2014). The technical program has been compiled using a high-quality peer-review process. After the paper deadline, the papers were distributed to the Area Chairs by the Track Chairs. This year, since some tracks had more submissions than expected, we recruited additional Area Chairs after the deadline in order to keep the review quality high. We also recruited an additional Track Chair in order to balance work load. Area Chairs assigned each paper to at least three reviewers and made the review summary reports. The Track Chairs together with the Program Chairs made the decision of acceptance based on the Area Chairs' reports. All decisions were made on at least two reviews and a summary report, most decisions on three reviews and the summary report. Finally, the Track Chairs composed the sessions, and the Program Chairs scheduled them and assigned rooms. The papers submitted by Program Chairs and Track Chairs were handled in a special Track, without influence from these. This year we also increased the number of pages for each paper from 4 to 6 in order to further increase the scientific quality of the conference by enabling the authors to explain their research in more detail.All of this review process is the result of dedicated hard work by members of various conference committees and additional volunteers, and we would like to thank all of them for their generous efforts. Especially, we gratefully acknowledge the fundamental contribution of the Track Chairs and Area Chairs, who directly supervised the review process carried out by 89 Area Chairs and more than 1,300 reviewers, who timely delivered high-quality reviews and decision reports. This year we received a total of 1,409 paper submissions from 46 countries. 198 papers were accepted as oral presentations and 594 papers were accepted as poster presentations. We arranged 42 sessions for oral presentations and 11 poster sessions. This year, we gave the poster presenters the opportunity to submit a one-page teaser in power-point and they were broadcasted on the monitors in the conference center. We organized two prize lectures and five invited talks, including articles in the proceedings.We expect this conference to be very lively and exciting form for all of us to meet, inspire and be inspiring in many presentations and discussions. We wish you a memorable stay in Stockholm.

Demand response (DR) can provide a cost-effect approach for reducing peak loads while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasingly integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the main power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium  is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. To obtain a SSCN equilibrium, sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of proposed model is illustrated by extensive simulations.

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