## Convex multi-task feature learning (2007)

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Venue: | MACHINE LEARNING |

Citations: | 145 - 15 self |

### BibTeX

@INPROCEEDINGS{Argyriou07convexmulti-task,

author = {Andreas Argyriou and Theodoros Evgeniou and Massimiliano Pontil},

title = {Convex multi-task feature learning},

booktitle = {MACHINE LEARNING},

year = {2007},

publisher = {press}

}

### Years of Citing Articles

### OpenURL

### Abstract

We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the former step it learns task-specific functions and in the latter step it learns common-across-tasks sparse representations for these functions. We also provide an extension of the algorithm which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned features common across related tasks. Our algorithm can also be used, as a special case, to simply select – not learn – a few common variables across the tasks.

### Citations

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Citation Context ...blem related to (14) has been presented in [2]. Theorem 4. If W is an optimal solution of problem (14) then for every t ∈ NT there exists a vector ct ∈ R mT such that wt = T� s=1 i=1 m� (ct)siϕ(xsi). =-=(15)-=- Proof. Let L = span{ϕ(xsi) : s ∈ NT , i ∈ Nm}. We can write wt = pt + nt , t ∈ NT where pt ∈ L and nt ∈ L ⊥ . Hence W = P + N, where P is the matrix with columns pt and N the matrix with columns nt. ... |

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Citation Context ... many supervised learning tasks. In particular, we develop a novel non-convex multi-task generalization of the 1-norm regularization known to provide sparse variable selection in the single-task case =-=[20, 27, 40]-=-. Our method learns a few features common across the tasks using a novel regularizer which both couples the tasks and enforces sparsity. These features are orthogonal functions in a prescribed reprodu... |

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Citation Context ...tion of Algorithm 1. 5.1 A Representer Theorem We begin by restating our optimization problem in the more general case when the tasks’ functions belong to a reproducing kernel Hilbert space, see e.g. =-=[7, 37, 44]-=- and references therein. Formally, we now wish to learn T regression functions ft, t ∈ NT of the form ft(x) = 〈at, U ⊤ ϕ(x)〉 = 〈wt, ϕ(x)〉 , x ∈ R d , where ϕ : R d → R M is a prescribed feature map. T... |

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Citation Context ...zation methods have been derived for the simpler problem of feature selection [31], prior work on multi-task feature learning has been based on more complex optimization problems which are not convex =-=[3, 9, 18]-=- and, so, these methods are not guaranteed to converge to a global minimum. In particular, in [9, 18] different neural networks with one or more hidden layers are trained for each task and they all sh... |

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Citation Context ... many supervised learning tasks. In particular, we develop a novel non-convex multi-task generalization of the 1-norm regularization known to provide sparse variable selection in the single-task case =-=[20, 27, 40]-=-. Our method learns a few features common across the tasks using a novel regularizer which both couples the tasks and enforces sparsity. These features are orthogonal functions in a prescribed reprodu... |

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Citation Context ...y be pursued in the context of multi-task learning include multivariate linear models in statistics such as reduced rank regression [30], partial least squares [45] and canonical correlation analysis =-=[29]-=- (see also [16]). These methods are based on generalized eigenvalue problems – see, for example, [13, Chapter 4] for a nice review. They have also been extended in an RKHS setting, see, for example, [... |

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Citation Context ...ed learning (e.g., using 1-norm regularization) or for unsupervised learning (e.g., using principal component analysis (PCA) or independent component analysis (ICA)), there has been only limited work =-=[3, 9, 31, 48]-=- in the multi-task supervised learning setting. In this paper, we present a novel method for learning sparse representations common across many supervised learning tasks. In particular, we develop a n... |

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Citation Context ...ting a specific object in images is treated as a single supervised learning task. Images of different objects may share a number of features that are different from the pixel representation of images =-=[28, 41, 43]-=-. In modeling users/consumers’ preferences [1, 33], there may be common product features (e.g., for cars, books, webpages, consumer electronics, etc) that are considered to be important by a number of... |

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Citation Context ...] (see also [16]). These methods are based on generalized eigenvalue problems – see, for example, [13, Chapter 4] for a nice review. They have also been extended in an RKHS setting, see, for example, =-=[11, 26]-=- and references therein. Although these methods have proved useful in practical applications, they require that the same input examples are shared by all the tasks. On the contrary, our approach does ... |

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Citation Context ...=1 where we have defined �W �tr := trace(W W ⊤ ) 1 2 . The expression �W �tr in the regularizer is called the trace norm. It can also be expressed as the sum of the singular values of W . As shown in =-=[23]-=-, the trace norm is the convex envelope of rank(W ) in the unit ball, which gives another interpretation of the relationship between the rank and γ in our experiments. Solving this problem directly is... |

160 | Learning multiple tasks with kernel methods
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Citation Context ...re we know what the underlying features used in all tasks are) and real datasets, also using our nonlinear generalization of the proposed method. The results show that in agreement with previous work =-=[3, 8, 9, 10, 19, 21, 31, 37, 38, 43, 46, 47, 48]-=- multi-task learning improves performance relative to single-task learning when the tasks are related. More importantly, the results confirm that when the tasks are related in the way we define in thi... |

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Citation Context ...rnating minimization strategy of Algorithm 1, which is simple tosConvex Multi-Task Feature Learning 15 implement and natural to interpret. We also note here that a similar problem has been studied in =-=[42]-=- for the particular case of an SVM loss function. It was shown there that the optimization problem can be solved through an equivalent semi-definite programming problem. We will further discuss relati... |

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Citation Context ...zation methods have been derived for the simpler problem of feature selection [31], prior work on multi-task feature learning has been based on more complex optimization problems which are not convex =-=[3, 9, 18]-=- and, so, these methods are not guaranteed to converge to a global minimum. In particular, in [9, 18] different neural networks with one or more hidden layers are trained for each task and they all sh... |

150 |
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Citation Context ...tion of Algorithm 1. 5.1 A Representer Theorem We begin by restating our optimization problem in the more general case when the tasks’ functions belong to a reproducing kernel Hilbert space, see e.g. =-=[7, 37, 44]-=- and references therein. Formally, we now wish to learn T regression functions ft, t ∈ NT of the form ft(x) = 〈at, U ⊤ ϕ(x)〉 = 〈wt, ϕ(x)〉 , x ∈ R d , where ϕ : R d → R M is a prescribed feature map. T... |

148 | Convex Analysis and Nonlinear Optimization: Theory and Examples
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Citation Context ...Modifying slightly to account for the feature map, we obtain the problems16 Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil � T� m� min L(yti, 〈wt, ϕ(xti)〉) + γ�W � 2 tr : W ∈ R d×T � . =-=(14)-=- t=1 i=1 This problem can be viewed as a generalization of the standard 2norm regularization problem. Indeed, in the case t = 1 the trace norm �W �tr is simply equal to �w1�2. In this case, it is well... |

147 | Multi-task feature learning
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Citation Context ...3 . Key words: Collaborative Filtering, Inductive Transfer, Kernels, MultiTask Learning, Regularization, Transfer Learning, Vector-Valued Functions. 3 This is a longer version of the conference paper =-=[4]-=-. It includes new theoretical and experimental results.s2 Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil 1 Introduction We study the problem of learning data representations that are co... |

114 | Task clustering and gating for bayesian multitask learning
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Citation Context ...re we know what the underlying features used in all tasks are) and real datasets, also using our nonlinear generalization of the proposed method. The results show that in agreement with previous work =-=[3, 8, 9, 10, 19, 21, 31, 37, 38, 43, 46, 47, 48]-=- multi-task learning improves performance relative to single-task learning when the tasks are related. More importantly, the results confirm that when the tasks are related in the way we define in thi... |

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Citation Context ...re we know what the underlying features used in all tasks are) and real datasets, also using our nonlinear generalization of the proposed method. The results show that in agreement with previous work =-=[3, 8, 9, 10, 19, 21, 31, 37, 38, 43, 46, 47, 48]-=- multi-task learning improves performance relative to single-task learning when the tasks are related. More importantly, the results confirm that when the tasks are related in the way we define in thi... |

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Citation Context ...n. Other interesting approaches which may be pursued in the context of multi-task learning include multivariate linear models in statistics such as reduced rank regression [30], partial least squares =-=[45]-=- and canonical correlation analysis [29] (see also [16]). These methods are based on generalized eigenvalue problems – see, for example, [13, Chapter 4] for a nice review. They have also been extended... |

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Citation Context ... the context of multi-task learning include multivariate linear models in statistics such as reduced rank regression [30], partial least squares [45] and canonical correlation analysis [29] (see also =-=[16]-=-). These methods are based on generalized eigenvalue problems – see, for example, [13, Chapter 4] for a nice review. They have also been extended in an RKHS setting, see, for example, [11, 26] and ref... |

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Citation Context |

68 | Multi-task Feature and Kernel Selection for Svms
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Citation Context ...ed learning (e.g., using 1-norm regularization) or for unsupervised learning (e.g., using principal component analysis (PCA) or independent component analysis (ICA)), there has been only limited work =-=[3, 9, 31, 48]-=- in the multi-task supervised learning setting. In this paper, we present a novel method for learning sparse representations common across many supervised learning tasks. In particular, we develop a n... |

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Citation Context ...ting a specific object in images is treated as a single supervised learning task. Images of different objects may share a number of features that are different from the pixel representation of images =-=[28, 41, 43]-=-. In modeling users/consumers’ preferences [1, 33], there may be common product features (e.g., for cars, books, webpages, consumer electronics, etc) that are considered to be important by a number of... |

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Citation Context ...ays a role similar to that of the barrier used in interior-point methods. In Appendix A, we prove that the optimal solution of problem (12) is given by Dε(W ) = (W W ⊤ + εI) 1 2 trace(W W ⊤ + εI) 1 2 =-=(13)-=- � and the optimal value equals trace(W W ⊤ + εI) 1 �2 2 . In the same appendix, we also show that for ε = 0, equation (13) gives the minimizer of the function R(W, ·) subject to the constraints in pr... |

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Citation Context ... supervised learning task. Images of different objects may share a number of features that are different from the pixel representation of images [28, 41, 43]. In modeling users/consumers’ preferences =-=[1, 33]-=-, there may be common product features (e.g., for cars, books, webpages, consumer electronics, etc) that are considered to be important by a number of people (we consider modeling an individual’s pref... |

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Citation Context ...Learning 13 In the second step, we keep matrix W fixed, and minimize Rε with respect to D. To this end, we solve the problem � T� min 〈wt, D −1 wt〉 + ε trace(D −1 ) : D ∈ S d � ++, trace(D) ≤ 1 . t=1 =-=(12)-=- The term trace(D−1 ) keeps the D-iterates of the algorithm at a certain distance from the boundary of Sd + and plays a role similar to that of the barrier used in interior-point methods. In Appendix ... |

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Citation Context ...ting a specific object in images is treated as a single supervised learning task. Images of different objects may share a number of features that are different from the pixel representation of images =-=[28, 41, 43]-=-. In modeling users/consumers’ preferences [1, 33], there may be common product features (e.g., for cars, books, webpages, consumer electronics, etc) that are considered to be important by a number of... |

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Citation Context ..., measured according to a prescribed loss function L : R × R → R+ which is convex in the second argument 5 A similar regularization function, but without matrix U, was also independently developed by =-=[39]-=- for the purpose of multi-task feature selection – see problem (5) below.s6 Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 √ T 2 4 6 8 10 12 1... |

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Citation Context ...ed learning (e.g., using 1-norm regularization) or for unsupervised learning (e.g., using principal component analysis (PCA) or independent component analysis (ICA)), there has been only limited work =-=[3, 9, 31, 48]-=- in the multi-task supervised learning setting. In this paper, we present a novel method for learning sparse representations common across many supervised learning tasks. In particular, we develop a n... |

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Citation Context ... many supervised learning tasks. In particular, we develop a novel non-convex multi-task generalization of the 1-norm regularization known to provide sparse variable selection in the single-task case =-=[20, 27, 40]-=-. Our method learns a few features common across the tasks using a novel regularizer which both couples the tasks and enforces sparsity. These features are orthogonal functions in a prescribed reprodu... |

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Citation Context ... supervised learning task. Images of different objects may share a number of features that are different from the pixel representation of images [28, 41, 43]. In modeling users/consumers’ preferences =-=[1, 33]-=-, there may be common product features (e.g., for cars, books, webpages, consumer electronics, etc) that are considered to be important by a number of people (we consider modeling an individual’s pref... |

40 | Learning convex combinations of continuously parameterized basic kernels
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Citation Context ...hich is convex in the second argument 5 A similar regularization function, but without matrix U, was also independently developed by [39] for the purpose of multi-task feature selection – see problem =-=(5)-=- below.s6 Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 √ T 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 1... |

35 | Fast rates for regularized least squares algorithm
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Citation Context ...rely on this assumption. Our work may be extended in different directions. First, it would be interesting to carry out a learning theory analysis of the algorithms presented in this paper. Results in =-=[17, 35]-=- may be useful for this purpose. Another interesting question is to study how the solution of our algorithms depends on the regularization parameter and investigates34 Andreas Argyriou, Theodoros Evge... |

32 |
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Citation Context ... this result to the more general form (14). Our proof is connected to the theory of operator monotone functions. We note that a representer theorem for a problem related to (14) has been presented in =-=[2]-=-. Theorem 4. If W is an optimal solution of problem (14) then for every t ∈ NT there exists a vector ct ∈ R mT such that wt = T� s=1 i=1 m� (ct)siϕ(xsi). (15) Proof. Let L = span{ϕ(xsi) : s ∈ NT , i ∈... |

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Citation Context ...with any convex loss function. Other interesting approaches which may be pursued in the context of multi-task learning include multivariate linear models in statistics such as reduced rank regression =-=[30]-=-, partial least squares [45] and canonical correlation analysis [29] (see also [16]). These methods are based on generalized eigenvalue problems – see, for example, [13, Chapter 4] for a nice review. ... |

31 | The convex analysis of unitarily invariant matrix functions - Lewis - 1995 |

22 | Bounds for linear multi-task learning
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Citation Context ...rely on this assumption. Our work may be extended in different directions. First, it would be interesting to carry out a learning theory analysis of the algorithms presented in this paper. Results in =-=[17, 35]-=- may be useful for this purpose. Another interesting question is to study how the solution of our algorithms depends on the regularization parameter and investigates34 Andreas Argyriou, Theodoros Evge... |

20 | A machine learning approach to conjoint analysis
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Citation Context ... know that W = UΣQ , where T ×δ′ U ∈ RM×δ′ , Σ ∈ Sδ′ ++ diagonal, Q ∈ R orthogonal, δ ′ ≤ δ, and the columns of U are the significant features learned. From this and (21) we obtain that U = � ΦBQΣ −1 =-=(22)-=- and Σ and Q can be computed from QΣ 2 Q ⊤ = W ⊤ W = B ⊤ � Φ ⊤ � ΦB . Finally, the coefficient matrix A can be computed from W = UA, (21) and (22), yielding ⎛ ⎞ A = ⎝ ΣQ⊤ ⎠ . 0 The computational cost ... |

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Citation Context ...] (see also [16]). These methods are based on generalized eigenvalue problems – see, for example, [13, Chapter 4] for a nice review. They have also been extended in an RKHS setting, see, for example, =-=[11, 26]-=- and references therein. Although these methods have proved useful in practical applications, they require that the same input examples are shared by all the tasks. On the contrary, our approach does ... |

5 |
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Citation Context ...kernel. 6.3 School Data We have also tested our algorithms on the data from the Inner London Education Authority 7 . This data set has been used in previous work on multitask learning, for example in =-=[8, 21, 24]-=-. It consists of examination scores of 15362 students from 139 secondary schools in London during the years 1985, 1986 and 1987. Thus, there are 139 tasks, corresponding to predicting student performa... |

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Citation Context ... many components of the learned vector at are zero, see [20] and references therein. Moreover, the number of nonzero components of a solution of problem (3) is typically a nonincreasing function of γ =-=[36]-=-.sConvex Multi-Task Feature Learning 7 Since we do not simply want to select the features but also learn them, we further minimize the function E over U. Therefore, our approach for multi-task feature... |

4 |
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Citation Context ...ny convex loss function. Our work may be extended in different directions. First, it would be interesting to carry out a learning theory analysis of the algorithms presented in this paper. Results in =-=[12, 25]-=- may be useful for this purpose. Another interesting question is to study how the solutions of our algorithm depend on the regularization parameter and investigate conditions which ensure that the num... |

1 |
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Citation Context ...r. We also have that 〈wt, ϕ(xti)〉 = 〈pt, ϕ(xti)〉. Thus, we conclude that whenever W is optimal, N must be zero. ⊓⊔ We also note that this theorem can be extended to a general family of spectral norms =-=[6]-=-. An alternative way to write equation (15), using matrix notation, is to express W as a multiple of the input matrix. The latter is the matrix Φ ∈ R M×mT whose (t, i)-th column is the vector ϕ(xti) ∈... |