Although many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made in federated settings in which multivariate time series data are heterogeneously distributed among different edge devices while data sharing is prohibited. In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) for learning local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device. Next, a constrained clustering mechanism (FedCC) is employed on the centralized server to align and aggregate the parameters of different local exemplar modules to obtain a unified global exemplar module. Finally, the global exemplar module is deployed together with a shared feature encoder to each edge device, and anomaly detection is conducted by examining the compatibility of testing data to the exemplar module. Fed-ExDNN captures local normal time series patterns with ExDNN and aggregates these patterns by FedCC, and thus can handle the heterogeneous data distributed over different edge devices simultaneously. Thoroughly empirical studies on six public datasets show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques, respectively.

Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attention on domain generalization (DG), which aims to the model's generalization ability to out-of-distribution. Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains; and (2) can suffer from feature space over collapse which can threaten generalization performance. To address these limitations, we propose localized adversarial domain generalization with space compactness maintenance (LADG) which constitutes two major contributions. First, we propose an adversarial localized classifier as the domain discriminator, along with a principled primary branch. This constructs a min-max game whereby the aim of the featurizer is to produce locally mixed domains. Second, we propose to use a coding-rate loss to alleviate feature space over collapse. We conduct comprehensive experiments on the Wilds DG benchmark to validate our approach, where LADG outperforms leading competitors on most datasets.

Visual sentiment analysis has received increasing attention in recent years. However, the quality of the dataset is a concern because the sentiment labels are crowd-sourcing, subjective, and prone to mistakes. This poses a severe threat to the data-driven models especially the deep neural networks. The deep models would generalize poorly on the testing cases when trained to over-fit the training samples with noisy sentiment labels. Inspired by the recent progress on learning with noisy labels, we propose a robust learning method to perform robust visual sentiment analysis. Our method relies on an external memory to aggregate and filter noisy labels during training and thus can prevent the model from overfitting the noisy cases. The memory is composed of the prototypes with corresponding labels, both of which can be updated online. We establish a benchmark for visual sentiment analysis with label noise using publicly available datasets. The experimental results comprehensively show the effectiveness of our method.

There are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), etc. We find that LPP and SR are equivalent if the symmetric similarity matrix is doubly stochastic, Positive Semi-Definite (PSD) and with rank$p$, where$p$is the reduced dimension. Since solving SR is believed faster than solving LPP based on some related literature, the discovery promotes us to seek to construct such specific similarity matrix to speed up LPP solving procedures. We then propose an unsupervised linear method called Unsupervised Large Graph Embedding (ULGE). ULGE starts with a similar idea as LPP but adopts an efficient approach to construct anchor-based similarity matrix and then performs spectral analysis on it. Moreover, since conventional anchor generation strategies suffer kinds of problems, we propose an efficient and effective anchor generation strategy, called Balanced$K$-means based Hierarchical$K$-means (BHKH). The computational complexity of ULGE can reduce to$O(ndm)$, which is a significant improvement compared to conventional methods need$O(n^2d)$at least, where$n$,$d$and$m$are the number of samples, dimensions, and anchors, respectively. Extensive experiments on several publicly available datasets demonstrate the efficiency and effectiveness of the proposed method.

With the growth of online shopping for fashion products, accurate fashion recommendation has become a critical problem. Meanwhile, social networks provide an open and new data source for personalized fashion analysis. In this work, we study the problem of personalized fashion recommendation from social media data, i.e. recommending new outfits to social media users that fit their fashion preferences. To this end, we present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. To extract features from multi-modal street-view fashion items, we propose an embedding module that performs multi-modality feature extraction and cross-modality gated fusion. To validate the effectiveness of our approach, we collect a real-world social media dataset. Extensive experiments on the collected dataset show the superior performance of our proposed approach.

Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset’s bias information. As a result, the obtained model would generalize poorly and even mislead the decision process in real-life applications. We propose to remove the bias information misused by the target task with a crosssample adversarial debiasing (CSAD) method. CSAD explicitly extracts target and bias features disentangled from the latent representation generated by a feature extractor and then learns to discover and remove the correlation between the target and bias features. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. Moreover, we propose joint content and local structural representation learning to boost mutual information estimation for better performance. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches.

Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully relied. We propose an unsupervised feature selection method which conducts feature selection and local structure learning simultaneously. Moreover, we add an important constraint on the similarity matrix to allow it to capture more accurate information of the data structure. To perform feature selection, orthogonal constraint and `2;p-norm are adopted on the projection matrix. An efficient and simple algorithm is derived to tackle the problem. We conduct comprehensive experiments on various benchmark data sets, including handwritten digit, face image, and biomedical data, to validate the effectiveness of the proposed approach.

Parkinson's Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. The high-resolution longitudinal activity data collected by smartphone applications nowadays make it possible to conduct remote and convenient health assessment. However, out-of-lab tests often suffer from poor quality controls as well as irregularly collected observations, leading to noisy test results. To address these issues, we propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild. The proposed method first synchronizes discrete activity tests into multimodal features at unified time points. Next, it distills and enriches local and global representations from noisy data across modalities and temporal observations by two attention modules. With the proposed mechanisms, our model is capable of handling noisy observations and at the same time extracting refined temporal features for improved prediction performance. Quantitative and qualitative results on a large public dataset demonstrate the effectiveness of the proposed approach.

There are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), etc. LPP and SR are two different linear spectral based methods, however, we discover that LPP and SR are equivalent, if the symmetric similarity matrix is doubly stochastic, Positive Semi-Definite (PSD) and with rank p, where p is the reduced dimension. The discovery promotes us to seek low-rank and doubly stochastic similarity matrix, we then propose an unsupervised linear dimensionality reduction method, called Unsupervised Large Graph Embedding (ULGE). ULGE starts with similar idea as LPP, it adopts an efficient approach to construct similarity matrix and then performs spectral analysis efficiently, the computational complexity can reduce to O(ndm), which is a significant improvement compared to conventional spectral based methods which need O(n^2d) at least, where n, d and m are the number of samples, dimensions and anchors, respectively. Extensive experiments on several public available data sets demonstrate the efficiency and effectiveness of the proposed method.

Spectral clustering has been regarded as a powerful tool for unsupervised tasks despite its excellent performance, the high computational cost has become a bottleneck which limits its application for large scale problems. Recent studies on anchor-based graph can partly alleviate the problem, however, it is still a great challenge to deal with such data with both high performance and high efficiency. In this paper, we propose Fast Spectral Clustering (FSC) to efficiently deal with large scale data. The proposed method first constructs anchor-based similarity graph with Balanced K-means based Hierarchical K-means (BKHK) algorithm, and then performs spectral analysis on the graph. The overall computational complexity is O(ndm), where n is the number of samples, d is the number of features, and m is the number of anchors. Comprehensive experiments on several large scale data sets demonstrate the effectiveness and efficiency of the proposed method.

Since amounts of unlabelled and high-dimensional data needed to be processed, unsupervised feature selection has become an important and challenging problem in machine learning. Conventional embedded unsupervised methods always need to construct the similarity matrix, which makes the selected features highly depend on the learned structure. However real world data always contain lots of noise samples and features that make the similarity matrix obtained by original data can't be fully relied. We propose an unsupervised feature selection approach which performs feature selection and local structure learning simultaneously, the similarity matrix thus can be determined adaptively. Moreover, we constrain the similarity matrix to make it contain more accurate information of data structure, thus the proposed approach can select more valuable features. An efficient and simple algorithm is derived to optimize the problem. Experiments on various benchmark data sets, including handwritten digit data, face image data and biomedical data, validate the effectiveness of the proposed approach.