Dictionary learning has been widely used in machine learning field to address many real-world applications, such as classification and denoising. In recent years, many new dictionary learning methods have been proposed. Most of them are designed to solve unsupervised problem without any prior information or supervised problem with the label information. But in real world, as usual, we can only obtain limited side information as prior information rather than label information. The existing methods don’t take into account the side information, let alone learning a good dictionary through using the side information. To tackle it, we propose a new unified unsupervised model which naturally integrates metric learning to enhance dictionary learning model with fully utilizing the side information. The proposed method updates metric space and dictionary adaptively and alternatively, which ensures learning optimal metric space and dictionary simultaneously. Besides, our method can also deal well with highdimensional data. Extensive experiments show the efficiency of our proposed method, and a better performance can be derived in real-world image clustering applications.

Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we propose a new approach to object ranking based on principles of analogical reasoning. More specifically, our inference pattern is formalized in terms of so-called analogical proportions and can be summarized as follows: Given objects A,B,C,D, if object A is known to be preferred to B, and C relates to D as A relates to B, then C is (supposedly) preferred to D. Our method applies this pattern as a main building block and combines it with ideas and techniques from instance-based learning and rank aggregation. Based on first experimental results for data sets from various domains (sports, education, tourism, etc.), we conclude that our approach is highly competitive. It appears to be specifically interesting in situations in which the objects are coming from different subdomains, and which hence require a kind of knowledge transfer.

The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.

This paper studies the problem of linking string mentions from web tables in one language to the corresponding named entities in a knowledge base written in another language, which we call the cross-lingual table linking task. We present a joint statistical model to simultaneously link all mentions that appear in one table. The framework is based on neural networks, aiming to bridge the language gap by vector space transformation and a coherence feature that captures the correlations between entities in one table. Experimental results report that our approach improves the accuracy of cross-lingual table linking by a relative gain of 12.1%. Detailed analysis of our approach also shows a positive and important gain brought by the joint framework and coherence feature.

Investigators say a homeless man accused of killing his wife, dismembering her body and riding with the remains in a suitcase aboard on a light-rail train in Los Angeles didn't draw any attention from fellow passengers.

LOS ANGELES – Investigators believe a homeless man killed his wife in an abandoned restaurant, chopped up her body, stuffed it into a suitcase and then calmly rode with it aboard a train before he burned her remains in a parking lot, Los Angeles police said Tuesday. After Valentino Gutierrez killed his wife last week in a shuttered restaurant in Pasadena, he dismembered her body, stuffed her remains into a large suitcase and boarded a light-rail train at a nearby station, Deputy Chief Justin Eisenberg said. Gutierrez, 56, who was charged Thursday with murder and arson, didn't draw any suspicion on the train and hopped aboard his bicycle after he exited the train. With the suitcase in tow, he peddled from a train station to the parking lot of a Home Depot in Los Angeles, where he set the suitcase ablaze. Detectives still haven't identified a motive in the case and coroner's officials have been unable to identify the burned remains.

We study the problem of distinguishing between two distributions on a metric space; i.e., given metric measure spaces $({\mathbb X}, d, \mu_1)$ and $({\mathbb X}, d, \mu_2)$, we are interested in the problem of determining from finite data whether or not $\mu_1$ is $\mu_2$. The key is to use pairwise distances between observations and, employing a reconstruction theorem of Gromov, we can perform such a test using a two sample Kolmogorov--Smirnov test. A real analysis using phylogenetic trees and flu data is presented.

Fire officials have deployed additional resources to be able to respond quickly in case blazes break out. Firefighters made quick work of a small brush fire that briefly threatened homes before dawn in Malibu.

We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called "calibration function" relating the excess surrogate risk to the actual risk. In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity. As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for general structured prediction.

Note, there are many variations in the way we calculate the term-frequency(tf) and inverse document frequency (idf), in this post we have seen one variation. Below images show as the other recommended variations of tf and idf, taken from wiki. Mathematically, closeness between two vectors is calculated by calculating the cosine angle between two vectors. In similar lines, we can calculate cosine angle between each document vector and the query vector to find its closeness. To find relevant document to the query term, we may calculate the similarity score between each document vector and the query term vector by applying cosine similarity .