Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of weight given to the unla- belled points, is extended in two important ways. First, and of fundamental impor- tance, the question over how to choose the amount of weight given to the unlabelled points is addressed. Then, the FSC approach is extended to mixtures of multivariate t-distributions. The first extension is essential because it makes FSC more readily applicable to real problems. The second, although less fundamental, demonstrates the efficacy of FSC beyond Gaussian mixture models. / Thesis / Master of Science (MSc)

Learning from unlabeled data is a topic of critical significance in machine learning, as the large datasets required to train ever-growing models are costly and impractical to annotate. Semi-Supervised Learning (SSL) methods aim to learn from a few labels and a large unlabeled dataset. In another approach, Domain Adaptation (DA) leverages data from a similar source domain to train a model for a target domain. This thesis focuses on Semi-Supervised Domain Adaptation (SSDA) for the dense task of semantic segmentation, where labels are particularly costly to obtain. SSDA has not received much attention yet, even though it has a great potential and represents a realistic scenario. The few existing SSDA methods for semantic segmentation reuse ideas from Unsupervised DA, despite the di↵erences between the two settings. This thesis proposes a new semantic segmentation framework designed particularly for the SSDA setting. The approach followed was to forego domain alignment and focus instead on enhancing clusterability of target domain features, an idea from SSL. The method is based on consistency regularization, combined with pixel contrastive learning and self-training. The proposed framework is found to be e↵ective not only in SSDA, but also in SSL. Ultimately, a unified solution for SSL and SSDA semantic segmentation is presented. Experiments were conducted on the target dataset of Cityscapes and source dataset of GTA5. The method proposed is competitive in both SSL and SSDA, and sets a new state-of-the-art for SSDA achieving a 65.6% mIoU (+4.4) on Cityscapes with 100 labeled samples. This thesis has an immediate impact on practical applications by proposing a new best-performing framework for the under-explored setting of SSDA. Furthermore, it also contributes towards the more ambitious goal of designing a unified solution for learning from unlabeled data. / Inlärning med hjälp av omärkt data är ett område av stor vikt inom maskininlärning. Detta på grund av att de stora datamängder som blivit nödvändiga för att träna konstant växande modeller både är kostsamma och opraktiska att implementera. Målet med Semi-Supervised Learning (SSL) är att kombinera ett fåtal etiketter med en stor mängd omärkt data för inlärning. Som ett annat tillvägagångssätt använder Domain Adaptation (DA) data från en liknande domän för att träna en annan måldomän. I Denna avhandling används Semi-Supervised Domain Adaptation (SSDA) för att utföra sådan semantisk segmentering, i vilken etiketter är särskilt kostsamma att erhålla. SSDA är ännu inte genererat mycket uppmärksamhet, även om det har en stor potential och representerar ett realistiskt scenario. De få metoder av SSDA som existerar för semantisk segmentering återanvänder idéer från Unsupervised DA, trots de olikheter som finns mellan de två modellerna. Denna avhandling föreslår ett nytt ramverk för semantisk segmentering, designat speciellt för SSDA modellen. Detta genom att försaka domänanpassning och i stället fokusera på att förbättra klusterbarheten av måldomänens egenskaper, en idé tagen från SSL. Metoden är baserad på konsistensregularisering, i kombination med pixelkontrastinlärning och självinlärning. Det föreslagna ramverket visar sig vara effektivt, inte bara för SSDA, men även för SSL. Till slut presenteras en enad lösning för semantisk segmentering med SLL och SSDA. Experiment utfördes på måldata från Cityscapes samt källdata från GTA5. Den föreslagna metoden är konkurrenskraftig både för SSL och SSDA, och blir världsledande för SSDA genom att uppnå 65,6% mIoU (+4,4) för Cityscapes med 100 märkta testdata. Denna avhandling har en omedelbar effekt gällande praktiska applikationer genom att föreslå ett nytt ”bäst resulterande” ramverk för dåligt utforskade inställningar av SSDA. Till yttermera visso bidrar avhandlingen även till det mer ambitiösa målet att designa en enad lösning för maskininlärning från omärkta data.

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Information fusion techniques have been widely applied in many applications including clustering, classification, detection and etc. The major objective is to improve the performance using information derived from multiple sources as compared to using information obtained from any of the sources individually. In our previous work, we demonstrated the performance improvement of Electroencephalography(EEG) based seizure detection using information fusion. In the detection problem, the optimal fusion rule is usually derived under the assumption that local decisions are conditionally independent given the hypotheses. However, due to the fact that local detectors observe the same phenomenon, it is highly possible that local decisions are correlated. To address the issue of correlation, we implement the fusion rule sub-optimally by first estimating the unknown parameters under one of the hypotheses and then using them as known parameters to estimate the rest of unknown parameters. In the aforementioned scenario, the hypotheses are uniquely defined, i.e., all local detectors follow the same labeling convention. However, in certain applications, the regions of interest (decisions, hypotheses, clusters and etc.) are not unique, i.e., may vary locally (from sources to sources). In this case, information fusion becomes more complicated. Historically, this problem was first observed in classification and clustering. In classification applications, the category information is pre-defined and training data is required. Therefore, a classification problem can be viewed as a detection problem by considering the pre-defined classes as the hypotheses in detection. However, information fusion in clustering applications is more difficult due to the lack of prior information and the correspondence problem caused by symbolic cluster labels. In the literature, information fusion in clustering problem is usually referred to as clustering ensemble problem. Most of the existing clustering ensemble methods are unsupervised. In this thesis, we proposed two semi-supervised clustering ensemble algorithms (SEA). Similar to existing ensemble methods, SEA consists of two major steps: the generation and fusion of base clusterings. Analogous to distributed detection, we propose a distributed clustering system which consists of a base clustering generator and a decision fusion center. The role of the base clustering generator is to generate multiple base clusterings for the given data set. The role of the decision fusion center is to combine all base clusterings into a single consensus clustering. Although training data is not required by conventional clustering algorithms (usually unsupervised), in many applications expert opinions are always available to label a small portion of data observations. These labels can be utilized as the guidance information in the fusion process. Therefore, we design two operational modes for the fusion center according to the absence or presence of the training data. In the unsupervised mode, any existing unsupervised clustering ensemble methods can be implemented as the fusion rule. In the semi-supervised mode, the proposed semi-supervised clustering ensemble methods can be implemented. In addition, a parallel distributed clustering system is also proposed to reduce the computational times of clustering high-volume data sets. Moreover, we also propose a new cluster detection algorithm based on SEA. It is implemented in the system to provide feedback information. When data observations from a new class (other than existing training classes) are detected, signal is sent out to request new training data or switching from the semi-supervised mode to the unsupervised mode. / Thesis / Doctor of Philosophy (PhD)

Various approaches for discriminant analysis of longitudinal data are investigated, with some focus on model-based approaches. The latter are typically based on the modi ed Cholesky decomposition of the covariance matrix in a Gaussian mixture; however, non-Gaussian mixtures are also considered. Where applicable, the Bayesian information criterion is used to select the number of components per class. The various approaches are demonstrated on real and simulated data. / Thesis / Master of Science (MSc)

Self-supervised learning models, which are called foundation models, have achieved great success in computer vision. Meanwhile, the limited access to labeled data has driven the development of self-supervised methods in remote sensing tasks. In remote sensing image change detection, the generative models are extensively utilized in unsupervised binary change detection tasks, while they overly focus on pixels rather than on abstract feature representations. In addition, the state-of-the-art satellite image time series change detection approaches fail to effectively leverage the spatial-temporal information of image time series or generalize well to unseen scenarios. Similarly, in the context of multimodal remote sensing data fusion, the recent successes of deep learning techniques mainly focus on specific tasks and complete data fusion paradigms. These task-specific models lack of generalizability to other remote sensing tasks and become overfitted to the dominant modalities. Moreover, they fail to handle incomplete modalities inputs and experience severe degradation in downstream tasks. To address these challenges associated with individual supervised learning models, this thesis presents two novel contributions to self-supervised learning models on remote sensing image change detection and multimodal remote sensing data fusion. The first contribution proposes a bi-temporal / multi-temporal contrastive change detection framework, which employs contrastive loss on image patches or superpixels to get fine-grained change maps and incorporates an uncertainty method to enhance the temporal robustness. In the context of satellite image time series change detection, the proposed approach improves the consistency of pseudo labels through feature tracking and tackles the challenges posed by seasonal changes in long-term remote sensing image time series using supervised contrastive loss and the random walk loss in ConvLSTM. The second contribution develops a self-supervised multimodal RS data fusion framework, with a specific focus on addressing the incomplete multimodal RS data fusion challenges in downstream tasks. Within this framework, multimodal RS data are fused by applying a multi-view contrastive loss at the pixel level and reconstructing each modality using others in a generative way based on MultiMAE. In downstream tasks, the proposed approach leverages a random modality combination training strategy and an attention block to enable fusion across modal-incomplete inputs. The thesis assesses the effectiveness of the proposed self-supervised change detection approach on single-sensor and cross-sensor datasets of SAR and multispectral images, and evaluates the proposed self-supervised multimodal RS data fusion approach on the multimodal RS dataset with SAR, multispectral images, DEM and also LULC maps. The self-supervised change detection approach demonstrates improvements over state-of-the-art unsupervised change detection methods in challenging scenarios involving multi-temporal and multi-sensor RS image change detection. Similarly, the self-supervised multimodal remote sensing data fusion approach achieves the best performance by employing an intermediate fusion strategy on SAR and optical image pairs, outperforming existing unsupervised data fusion approaches. Notably, in incomplete multimodal fusion tasks, the proposed method exhibits impressive performance on all modal-incomplete and single modality inputs, surpassing the performance of vanilla MultiViT, which tends to overfit on dominant modality inputs and fails in tasks with single modality inputs.

Dealing with large amounts of unlabeled data is a very challenging task. Recently, many different approaches have been proposed to leverage this data for training many machine learning models. Among them, self-supervised learning appears as an efficient solution capable of training powerful and generalizable models. More specifically, instead of relying on human-generated labels, it proposes training objectives that use ``labels'' generated from the data itself, either via data augmentation or by masking the data in some way and trying to reconstruct it. Apart from being able to train models from scratch, self-supervised methods can also be used in specific applications to further improve a pre-trained model. In this thesis, we propose to leverage self-supervised methods in novel ways to tackle different application scenarios. We present four published papers: an open-source library for self-supervised learning that is flexible, scalable, and easy to use; two papers tackling unsupervised domain adaptation in action recognition; and one paper on self-supervised learning for continual learning. The published papers highlight that self-supervised techniques can be leveraged for many scenarios, yielding state-of-the-art results.

Fuzzy logic systems are widely used for control, system identification, and pattern recognition problems. In order to maximize their performance, it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Some data to approximate are commonly available and yield what is called the supervised learning problem. In this problem we typically wish to minimize the sum of the squares of errors in approximating the data. We first introduce fuzzy logic systems and the supervised learning problem that, in effect, is a nonlinear optimization problem that at times can be non-differentiable. We review the existing approaches and discuss their weaknesses and the issues involved. We then focus on one of these problems, i.e., non-differentiability of the objective function, and show how current approaches that do not account for non-differentiability can diverge. Moreover, we also show that non-differentiability may also have an adverse practical impact on algorithmic performances. We reformulate both the supervised learning problem and piecewise linear membership functions in order to obtain a polynomial or factorable optimization problem. We propose the application of a global nonconvex optimization approach, namely, a reformulation and linearization technique. The expanded problem dimensionality does not make this approach feasible at this time, even though this reformulation along with the proposed technique still bears a theoretical interest. Moreover, some future research directions are identified. We propose a novel approach to step-size selection in batch training. This approach uses a limited memory quadratic fit on past convergence data. Thus, it is similar to response surface methodologies, but it differs from them in the type of data that are used to fit the model, that is, already available data from the history of the algorithm are used instead of data obtained according to an experimental design. The step-size along the update direction (e.g., negative gradient or deflected negative gradient) is chosen according to a criterion of minimum distance from the vertex of the quadratic model. This approach rescales the complexity in the step-size selection from the order of the (large) number of training data, as in the case of exact line searches, to the order of the number of parameters (generally lower than the number of training data). The quadratic fit approach and a reduced variant are tested on some function approximation examples yielding distributions of the final mean square errors that are improved (i.e., skewed toward lower errors) with respect to the ones in the commonly used pattern-by-pattern approach. Moreover, the quadratic fit is also competitive and sometimes better than the batch training with optimal step-sizes, thus showing an improved performance of this approach. The quadratic fit approach is also tested in conjunction with gradient deflection strategies and memoryless variable metric methods, showing errors smaller by 1 to 7 orders of magnitude. Moreover, the convergence speed by using either the negative gradient direction or a deflected direction is higher than that of the pattern-by-pattern approach, although the computational cost of the algorithm per iteration is moderately higher than the one of the pattern-by-pattern method. Finally, some directions for future research are identified. / Ph. D.

Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and which ones do not (cannot-link). These approaches require many such constraints before achieving good clustering performance because each constraint only provides weak cues about the desired clustering. In this work, we propose to use image attributes as a modality for the user to provide more informative cues. In particular, the clustering algorithm iteratively and actively queries a user with an image pair. Instead of the user simply providing a must-link/cannot-link constraint for the pair, the user also provides an attribute-based reasoning e.g. "these two images are similar because both are natural and have still water'' or "these two people are dissimilar because one is way older than the other''. Under the guidance of this explanation, and equipped with attribute predictors, many additional constraints are automatically generated. We demonstrate the effectiveness of our approach by incorporating the proposed attribute-based explanations in three standard semi-supervised clustering algorithms: Constrained K-Means, MPCK-Means, and Spectral Clustering, on three domains: scenes, shoes, and faces, using both binary and relative attributes. / Master of Science