Aproksimativni algoritmi za generisanje k-NN grafa

Nearest neighbor graphs are modeling proximity relationships between objects. They are widely used in many areas, primarily in machine learning, but also in information retrieval, biology, computer graphics,geographic information systems, etc. The focus of this thesis are knearest neighbor graphs (k-NNG), a special class of nearest neighbor graphs. Each node of k-NNG is connected with directed edges to its k nearest neighbors.A brute-force method for constructing k-NNG entails O(n 2 ) distance calculations. This thesis addresses the problem of more efficient k-NNG construction, achieved by approximation algorithms. The main challenge of an approximation algorithm for k-NNG construction is to decrease the number of distance calculations, while maximizing the approximation’s accuracy.NN-Descent is one such approximation algorithm for k-NNG construction, which reports excellent results in many cases. However, it does not perform well on high-dimensional data. The first part of this thesis summarizes the problem, and gives explanations for such a behavior. The second part introduces five new NN-Descent variants that aim to improve NN-Descent on high-dimensional data. The performance of the  proposed algorithms is evaluated with an experimental analysis.Finally, the third part of this thesis is dedicated to k-NNG update algorithms. Namely, in the real world scenarios data often change over time. If data change after k-NNG construction, the graph needs to be updated accordingly. Therefore, in this part of the thesis, two approximation algorithms for k-NNG updates are proposed. They are validated with extensive experiments on time series data.Graf najbližih suseda modeluje veze između objekata koji su međusobno bliski. Ovi grafovi se koriste u mnogim disciplinama, pre svega u mašinskom učenju, a potom i u pretraživanju informacija, biologiji, računarskoj grafici, geografskim informacionim sistemima, itd. Fokus ove teze je graf k najbližih suseda (k-NN graf), koji predstavlja posebnu klasu grafova najbližih suseda. Svaki čvor k-NN grafa je povezan usmerenim granama sa njegovih k najbližih suseda.Metod grube sile za generisanje k-NN grafova podrazumeva O(n 2 ) računanja razdaljina između dve tačke. Ova teza se bavi  problemom efikasnijeg generisanja k-NN grafova, korišćenjem aproksimativnih  algoritama.Glavni cilj aprokismativnih algoritama za generisanje k-NN grafova jeste smanjivanje ukupnog broja računanja razdaljina između dve tačke, uz održavanje visoke tačnosti krajnje aproksimacije.NN-Descent je jedan takav aproksimativni algoritam za generisanje k-NN grafova. Iako se pokazao kao veoma dobar u većini slučajeva, ovaj algoritam ne daje dobre rezultate nad visokodimenzionalnim podacima. Unutar prvog dela teze, detaljno je opisana suština problema i objašnjeni su razlozi za njegovo nastajaneU drugom delu predstavljeno je pet različitih modifikacija NN-Descent algoritma, koje za cilj imaju njegovo poboljšavanje pri radu nad visokodimenzionalnim podacima. Evaluacija ovih algoritama je data kroz eksperimentalnu analizu.Treći deo teze se bavi algoritmima za ažuriranje k-NN grafova. Naime,podaci se vrlo često menjaju  vremenom. Ukoliko se izmene podaci nad kojima je prethodno generisan k-NN graf, potrebno je graf ažurirati u skladu sa izmenama. U okviru ovog dela teze predložena su dva aproksimativna algoritma za ažuriranje k-NN grafova. Ovi algoritmi su evaluirani opširnim eksperimentima nad vremenskim serijama

Aproksimativni algoritmi za generisanje k-NN grafa

Nearest neighbor graphs are modeling proximity relationships between objects. They are widely used in many areas, primarily in machine learning, but also in information retrieval, biology, computer graphics,geographic information systems, etc. The focus of this thesis are knearest neighbor graphs (k-NNG), a special class of nearest neighbor graphs. Each node of k-NNG is connected with directed edges to its k nearest neighbors.A brute-force method for constructing k-NNG entails O(n 2 ) distance calculations. This thesis addresses the problem of more efficient k-NNG construction, achieved by approximation algorithms. The main challenge of an approximation algorithm for k-NNG construction is to decrease the number of distance calculations, while maximizing the approximation’s accuracy.NN-Descent is one such approximation algorithm for k-NNG construction, which reports excellent results in many cases. However, it does not perform well on high-dimensional data. The first part of this thesis summarizes the problem, and gives explanations for such a behavior. The second part introduces five new NN-Descent variants that aim to improve NN-Descent on high-dimensional data. The performance of the  proposed algorithms is evaluated with an experimental analysis.Finally, the third part of this thesis is dedicated to k-NNG update algorithms. Namely, in the real world scenarios data often change over time. If data change after k-NNG construction, the graph needs to be updated accordingly. Therefore, in this part of the thesis, two approximation algorithms for k-NNG updates are proposed. They are validated with extensive experiments on time series data.Graf najbližih suseda modeluje veze između objekata koji su međusobno bliski. Ovi grafovi se koriste u mnogim disciplinama, pre svega u mašinskom učenju, a potom i u pretraživanju informacija, biologiji, računarskoj grafici, geografskim informacionim sistemima, itd. Fokus ove teze je graf k najbližih suseda (k-NN graf), koji predstavlja posebnu klasu grafova najbližih suseda. Svaki čvor k-NN grafa je povezan usmerenim granama sa njegovih k najbližih suseda.Metod grube sile za generisanje k-NN grafova podrazumeva O(n 2 ) računanja razdaljina između dve tačke. Ova teza se bavi  problemom efikasnijeg generisanja k-NN grafova, korišćenjem aproksimativnih  algoritama.Glavni cilj aprokismativnih algoritama za generisanje k-NN grafova jeste smanjivanje ukupnog broja računanja razdaljina između dve tačke, uz održavanje visoke tačnosti krajnje aproksimacije.NN-Descent je jedan takav aproksimativni algoritam za generisanje k-NN grafova. Iako se pokazao kao veoma dobar u većini slučajeva, ovaj algoritam ne daje dobre rezultate nad visokodimenzionalnim podacima. Unutar prvog dela teze, detaljno je opisana suština problema i objašnjeni su razlozi za njegovo nastajaneU drugom delu predstavljeno je pet različitih modifikacija NN-Descent algoritma, koje za cilj imaju njegovo poboljšavanje pri radu nad visokodimenzionalnim podacima. Evaluacija ovih algoritama je data kroz eksperimentalnu analizu.Treći deo teze se bavi algoritmima za ažuriranje k-NN grafova. Naime,podaci se vrlo često menjaju  vremenom. Ukoliko se izmene podaci nad kojima je prethodno generisan k-NN graf, potrebno je graf ažurirati u skladu sa izmenama. U okviru ovog dela teze predložena su dva aproksimativna algoritma za ažuriranje k-NN grafova. Ovi algoritmi su evaluirani opširnim eksperimentima nad vremenskim serijama

Transductive Zero-Shot Action Recognition by Word-Vector Embedding

The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all categories. Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is zero-shot learning" (ZSL). To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training data. Existing ZSL studies focus primarily on still images, and attribute-based semantic representations. In this work, we explore word-vectors as the shared semantic space to embed videos and category labels for ZSL action recognition. This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video spacetime features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross-category domain shift. To solve this generalisation problem in ZSL action recognition, we investigate a series of synergistic strategies to improve upon the standard ZSL pipeline. Most of these strategies are transductive in nature which means access to testing data in the training phase.Comment: Accepted by IJC

Neural models for stepwise text illustration

In this thesis, we investigate the task of sequence-to-sequence (seq2seq) retrieval: given a sequence (of text passages) as the query, retrieve a sequence (of images) that best describes and aligns with the query. This is a step beyond the traditional cross-modal retrieval which treats each image-text pair independently and ignores broader context. Since this is a difficult task, we break it into steps. We start with caption generation for images in news articles. Different from traditional image captioning task where a text description is generated given an image, here, a caption is generated conditional on both image and the news articles where it appears. We propose a novel neural-networks based methodology to take into account both news article content and image semantics to generate a caption best describing the image and its surrounding text context. Our results outperform existing approaches to image captioning generation. We then introduce two new novel datasets, GutenStories and Stepwise Recipe datasets for the task of story picturing and sequential text illustration. GutenStories consists of around 90k text paragraphs, each accompanied with an image, aligned in around 18k visual stories. It consists of a wide variety of images and story content styles. StepwiseRecipe is a similar dataset having sequenced image-text pairs, but having only domain-constrained images, namely food-related. It consists of 67k text paragraphs (cooking instructions), each accompanied by an image describing the step, aligned in 10k recipes. Both datasets are web-scrawled and systematically filtered and cleaned. We propose a novel variational recurrent seq2seq (VRSS) retrieval model. xii The model encodes two streams of information at every step: the contextual information from both text and images retrieved in previous steps, and the semantic meaning of the current input (text) as a latent vector. These together guide the retrieval of a relevant image from the repository to match the semantics of the given text. The model has been evaluated on both the Stepwise Recipe and GutenStories datasets. The results on several automatic evaluation measures show that our model outperforms several competitive and relevant baselines. We also qualitatively analyse the model both using human evaluation and by visualizing the representation space to judge the semantical meaningfulness. We further discuss the challenges faced on the more difficult GutenStories and outline possible solutions

Solmujen sisäinen konnektiviteetti ja topologiset roolit toiminnallisissa aivoverkoissa

Many real-life phenomena consist of a number of interacting elements and can thus be modeled as a complex network. The human brain is an example of such a system where the neuronal information processing of the brain is characterized by interaction and information exchange between different brain regions. In this Thesis, we examine functional brain networks estimated from functional magnetic resonance imaging (fMRI) data. When defining network nodes, the small measurement units, voxels, are grouped to larger entities that represent supposedly functionally homogeneous brain regions referred to as Regions of Interest (ROIs). Despite their assumed homogeneity, it has been demonstrated that the voxels within a ROI exhibit spatially and temporally varying correlation structure. This gives rise to a concept referred to as internal connectivity. On the larger scale, the ROIs form a brain network where each ROI has its role in the structure of the network topology, i.e., a topological role. Topological roles have been suggested to be indicative of the node's functional specialization. On the other hand, it has been argued that internal connectivity may relate to the mechanisms the ROI uses to interact with its neighbors in the functional brain network. This Thesis combines these two ideas. To this end, we aim to predict the ROI's topological role from its internal connectivity features. We find that using internal connectivity features as model variables increases the classification accuracy in comparison to a baseline classifier. These results suggest that there is a relationship between internal connectivity and the ROI's topological role. This link provides a basis for faster and more computationally efficient topological role estimation. Further, it helps to better understand the mechanisms brain regions use to interact with each other. Both of these factors importantly increase our knowledge on brain function under different tasks and circumstances.Monet todellisen maailman ilmiöt koostuvat useista vuorovaikutuksessa olevista elementeistä, ja niitä voidaan mallintaa kompleksisina verkostoina. Ihmisaivot ovat esimerkki tällaisesta järjestelmästä, jossa aivojen hermosolutason tiedonkäsittely perustuu aivoalueiden väliseen vuorovaikutukseen ja tiedonvaihtoon. Diplomityössäni tutkin toiminnallisesta magneettikuvausdatasta rakennettuja toiminnallisia aivoverkkoja. Verkon solmuja määritettäessä pienet mittauselementit, vokselit, ryhmitellään isommiksi kokonaisuuksiksi, jotka edustavat toiminnallisesti yhtenäisiksi oletettuja aivoalueita (engl. Region of Interest, ROI). On kuitenkin osoitettu, että oletetusta yhtenäisyydestään huolimatta ROIden sisällä on monimuotoisia sekä paikallisesti että ajallisesti vaihtelevia korrelaatiorakenteita. Tästä syntyy sisäisen konnektiviteetin käsite, joka kuvaa ROI:n sisäistä korrelaatiorakennetta ja sen vaihtelua. Laajemmassa mittakaavassa ROI:t muodostavat aivoverkon, jossa jokaisella ROI:lla on verkon rakenteessa oma roolinsa, n.s. topologinen rooli. Topologisten roolien ajatellaan liittyvän ROI:den toiminnalliseen erikoistumiseen. On myös esitetty, että sisäinen konnektiviteetti liittyy niihin mekanismeihin, joiden avulla ROI vuorovaikuttaa naapureidensa kanssa toiminnallisessa aivoverkossa. Tämä diplomityö yhdistää nämä kaksi ajatusta: ROI:n topologista roolia pyritään ennustamaan sen sisäisen konnektiviteetin tekijöiden avulla. Tulokset osoittavat, että sisäisen konnektiviteetin tekijät parantavat ennustustarkkuutta verrattuna valistuneeseen arvaukseen perustuvaan pohjatasoluokittimeen. Tulokset osoittavat, että ROI:n sisäisen konnetiviteetin ja topologisten roolien välillä on yhteys. Tämä yhteys tarjoaa pohjan topologisten roolien nopeammalle ja laskennallisesti tehokkaammalle määrittämiselle ja lisää ymmärrystä niistä mekanismeista, joita ROI:t käyttävät vuorovaikuttaakseen toistensa kanssa. Nämä tekijät lisäävät tietoa aivojen toiminnasta eri tilanteissa ja tehtävissä