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Visual place recognition is essential for large-scale simultaneous localization and mapping (SLAM). Long-term robot operations across different time of the days, months, and seasons introduce new challenges from significant environment appearance variations. In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared ℓ2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the ℓ2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. In addition, because our solution is strictly orthogonal, the learned location representations can have better place recognition capabilities. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nordland data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications. 

Alzheimer’s disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer’s disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe.By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer’s Disease Neuroimaging Initiative(ADNI), we propose a newJoint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD. 

Traditional neuroimaging analysis, such as clustering the data collected for the Alzheimer's disease (AD), usually relies on the data from one single imaging modality. However, recent technology and equipment advancements provide with us opportunities to better analyze diseases, where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper we conduct a new study to make use of the data from different modalities/views. To achieve this goal, we propose a simple yet efficient method based on Non-negative Matrix Factorization (NMF) which can not only achieve better prediction performance but also deal with some data missing in some views. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method. 

Many existing studies on complex brain disorders, such as Alzheimer's Disease, usually employed regression analysis to associate the neuroimaging measures to cognitive status. However, whether these measures in multiple modalities have the predictive power to infer the trajectory of cognitive performance over time still remain under-explored. In this paper, we propose a high-order multi-modal multi-mask feature learning model to uncover temporal relationship between the longitudinal neuroimaging measures and progressive cognitive output scores. The regularizations through sparsity-induced norms implemented in the proposed learning model enable the selection of only a small number of imaging features over time and capture modality structures for multi-modal imaging markers. The promising experimental results in extensive empirical studies performed on the ADNI cohort have validated the effectiveness of the proposed method.