Progressive transductive learning pattern classification via single sphere

In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.

Two-way Markov random walk transductive learning algorithm

Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.

Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification

To create a green and healthy living environment,people have put forward higher requirements for the refined management of ecological resources.A variety of technologies,including satellite remote sensing,Internet of Things,artificial intelligence,and big data,can build a smart environmental monitoring system.Remote sensing image classification is an important research content in ecological environmental monitoring.Remote sensing images contain rich spatial information andmulti-temporal information,but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy.To solve this problem,this study develops a transductive transfer dictionary learning(TTDL)algorithm.In the TTDL,the source and target domains are transformed fromthe original sample space to a common subspace.TTDL trains a shared discriminative dictionary in this subspace,establishes associations between domains,and also obtains sparse representations of source and target domain data.To obtain an effective shared discriminative dictionary,triple-induced ordinal locality preserving term,Fisher discriminant term,and graph Laplacian regularization termare introduced into the TTDL.The triplet-induced ordinal locality preserving term on sub-space projection preserves the local structure of data in low-dimensional subspaces.The Fisher discriminant term on dictionary improves differences among different sub-dictionaries through intra-class and inter-class scatters.The graph Laplacian regularization term on sparse representation maintains the manifold structure using a semi-supervised weight graphmatrix,which can indirectly improve the discriminative performance of the dictionary.The TTDL is tested on several remote sensing image datasets and has strong discrimination classification performance.

Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

Survival analysis aims to predict the occurrence time of a particular event of interest,which is crucial for the prognosis analysis of diseases.Currently,due to the limited study period and potential losing tracks,the observed data inevitably involve some censored instances,and thus brings a unique challenge that distinguishes from the general regression problems.In addition,survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size problems.To address these challenges,we propose a novel multi-task regression learning model,i.e.,prior information guided transductive matrix completion(PigTMC)model,to predict the survival status of the new instances.Specifically,we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples,and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size data.In addition,we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival analysis.Furthermore,we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC model.Finally,the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

Task-specific Part Discovery for Fine-grained Few-shot Classification

Localizing discriminative object parts(e.g.,bird head)is crucial for fine-grained classification tasks,especially for the more challenging fine-grained few-shot scenario.Previous work always relies on the learned object parts in a unified manner,where they attend the same object parts(even with common attention weights)for different few-shot episodic tasks.In this paper,we propose that it should adaptively capture the task-specific object parts that require attention for each few-shot task,since the parts that can distinguish different tasks are naturally different.Specifically for a few-shot task,after obtaining part-level deep features,we learn a task-specific part-based dictionary for both aligning and reweighting part features in an episode.Then,part-level categorical prototypes are generated based on the part features of support data,which are later employed by calculating distances to classify query data for evaluation.To retain the discriminative ability of the part-level representations(i.e.,part features and part prototypes),we design an optimal transport solution that also utilizes query data in a transductive way to optimize the aforementioned distance calculation for the final predictions.Extensive experiments on five fine-grained benchmarks show the superiority of our method,especially for the 1-shot setting,gaining 0.12%,8.56%and 5.87%improvements over state-of-the-art methods on CUB,Stanford Dogs,and Stanford Cars,respectively.

Semi-supervised learning via manifold regularization

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

Effects of 5-hydroxymethyl Furfural Extracted from Rehmannia Glutinosa Libosch on the Expression of Signaling Molecules Relevant to Learning and Memory among Hippocampal Neurons Exposed to High Concentration of Corticosterone

Objective：To determine the effects of 5-hydroxymethyl furfural（5-HMF）,an extract of Rehmannia glutinosa Libosch,on several down-regulated signaling molecules involved in learning and memory in hippocampal neurons.Methods：After cultured for 7 days,primary hippocampal neurons were divided into 5 groups：normal,corticosterone model,RU38486,5-HMF,and donepezil group.Neuron survival rates were calculated 24 h later using SYT013-P1 double-fluorescence staining and an 3-（4,5-Dimethylthiazol-2-yl）-2,5-diphenyltetrazolium bromide（MTT）assay.β-galactosidase activity was also assayed.Protein expressed by the glucocorticoid receptor（GCR）,brainderived neurotrophic factor（BDNF）,and N-methyl-D-aspartate receptor 2B（NR2B）,as well as phosphorylationcyclic adenosine monophosphate（cAMP）response element binding protein（p-CREB）,phosphorylation-extracellular signal-regulated kinase（p-ERK）,and phosphorylation-synapsin（p-synapsin）were quantified with Western blot.Results：Hippocampal neuron survival rates and the above-mentioned proteins were dramatically decreased（P〈0.05）,β-galactosidase activity was significantly increased in the model group,but the effect was reversed by5-HMF,RU38486,and to a lesser extent by donepezil（P〈0.05）.Conclusion：5-HMF extracts from the Chinese herb Rehmannia glutinosa Libosch could protect hippocampal neurons from glucocorticoid injury and from down-regulated signaling molecules in the GCR-BDNF-NR2B-p-ERK-p-CREB-p-synapsin signal transduction pathway.

EncyCatalogRec: catalog recommendation for encyclopedia article completion

Online encyclopedias such as Wikipedia provide a large and growing number of articles on many topics.However,the content of many articles is still far from complete.In this paper,we propose Ency Catalog Rec,a system to help generate a more comprehensive article by recommending catalogs.First,we represent articles and catalog items as embedding vectors,and obtain similar articles via the locality sensitive hashing technology,where the items of these articles are considered as the candidate items.Then a relation graph is built from the articles and the candidate items.This is further transformed into a product graph.So,the recommendation problem is changed to a transductive learning problem in the product graph.Finally,the recommended items are sorted by the learning-to-rank technology.Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation in both warm-and cold-start scenarios.We have validated our approach by a case study.